{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/focal-loss","entry":"focal_loss","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":65,"n_papers_ran":38,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":46,"n_samples_ran":20,"n_samples_fingerprinted":6,"n_places":66,"n_places_pointer_only":20,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":5,"ran_fixture":5,"ran":8,"unverified":26},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2605.23254","paper":"/paper/arxiv-2605-23254","title":"CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2605.01065","paper":"/paper/arxiv-2605-01065","title":"A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"sjmeis/DP-Decompose-Distribute","path":"eval_scripts/adaptive_attacker.py","file_url":"https://github.com/sjmeis/DP-Decompose-Distribute/blob/HEAD/eval_scripts/adaptive_attacker.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd41c5fa76336833","mcp_get_code":{"code_sha256":"fd41c5fa76336833"}},{"arxiv_id":"2604.06468","paper":"/paper/arxiv-2604-06468","title":"Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"YuanjieSh/CMRM","path":"Multi_code_synthetic_noise/train/losses.py","file_url":"https://github.com/YuanjieSh/CMRM/blob/HEAD/Multi_code_synthetic_noise/train/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2603.26008","paper":"/paper/arxiv-2603-26008","title":"FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistants","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"bhosalems/FairLLaVA","path":"llava/model/language_model/llava_llama.py","file_url":"https://github.com/bhosalems/FairLLaVA/blob/HEAD/llava/model/language_model/llava_llama.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"c48f90c323496675","mcp_get_code":{"code_sha256":"c48f90c323496675"}},{"arxiv_id":"2603.01759","paper":"/paper/arxiv-2603-01759","title":"Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"doem97/metalora","path":"utils/losses.py","file_url":"https://github.com/doem97/metalora/blob/HEAD/utils/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2505.13778","paper":"/paper/coin-counting-the-invisible-reasoning-tokens","title":"CoIn: Counting the Invisible Reasoning Tokens in Commercial Opaque LLM APIs","date":"2025-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"case-lab-umd/llm-auditing-coin","path":"3_Block2Answer/train/loss_func.py","file_url":"https://github.com/case-lab-umd/llm-auditing-coin/blob/HEAD/3_Block2Answer/train/loss_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d00ea9e4c5542ee7","mcp_get_code":{"code_sha256":"d00ea9e4c5542ee7"}},{"arxiv_id":"2502.16060","paper":"/paper/single-channel-eeg-tokenization-through-time","title":"Single-Channel EEG Tokenization Through Time-Frequency Modeling","date":"2025-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jathurshan0330/TFM-Tokenizer","path":"utils/utils.py","file_url":"https://github.com/Jathurshan0330/TFM-Tokenizer/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4156bae98d1b1972","mcp_get_code":{"code_sha256":"4156bae98d1b1972"}},{"arxiv_id":"2410.21042","paper":"/paper/improving-visual-prompt-tuning-by-gaussian","title":"Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Keke921/GNM-PT","path":"loss.py","file_url":"https://github.com/Keke921/GNM-PT/blob/HEAD/loss.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b3f82e6c53766e7","mcp_get_code":{"code_sha256":"1b3f82e6c53766e7"}},{"arxiv_id":"2410.14672","paper":"/paper/bigr-harnessing-binary-latent-codes-for-image","title":"BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoosz/BiGR","path":"bae/binarylatent.py","file_url":"https://github.com/haoosz/BiGR/blob/HEAD/bae/binarylatent.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3164da5cbc56c361","mcp_get_code":{"code_sha256":"3164da5cbc56c361"}},{"arxiv_id":"2407.12568","paper":"/paper/ltrl-boosting-long-tail-recognition-via","title":"LTRL: Boosting Long-tail Recognition via Reflective Learning","date":"2024-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fistyee/LTRL","path":"model/loss.py","file_url":"https://github.com/fistyee/LTRL/blob/HEAD/model/loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2406.12638","paper":"/paper/efficient-and-long-tailed-generalization-for","title":"Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shijxcs/candle","path":"trainers/losses.py","file_url":"https://github.com/shijxcs/candle/blob/HEAD/trainers/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2406.06818","paper":"/paper/conformal-prediction-for-class-wise-coverage","title":"Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration","date":"2024-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanjiesh/rc3p","path":"train/losses.py","file_url":"https://github.com/yuanjiesh/rc3p/blob/HEAD/train/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2406.05261","paper":"/paper/split-and-fit-learning-b-reps-via-structure","title":"Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yilinliu77/nvdnet","path":"python/model.py","file_url":"https://github.com/yilinliu77/nvdnet/blob/HEAD/python/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"b20bcef51505bcf4","mcp_get_code":{"code_sha256":"b20bcef51505bcf4"}},{"arxiv_id":"2405.11756","paper":"/paper/erasing-the-bias-fine-tuning-foundation","title":"Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gank0078/FineSSL","path":"utils/losses.py","file_url":"https://github.com/Gank0078/FineSSL/blob/HEAD/utils/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2404.03015","paper":"/paper/dpft-dual-perspective-fusion-transformer-for","title":"DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection","date":"2024-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tumftm/dpft","path":"src/dprt/training/loss.py","file_url":"https://github.com/tumftm/dpft/blob/HEAD/src/dprt/training/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"abd213a9f852803b","mcp_get_code":{"code_sha256":"abd213a9f852803b"}},{"arxiv_id":"2403.20126","paper":"/paper/eclipse-efficient-continual-learning-in","title":"ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/ECLIPSE","path":"continual/method_wrapper/loss.py","file_url":"https://github.com/clovaai/ECLIPSE/blob/HEAD/continual/method_wrapper/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b9edb542ddeedb32","mcp_get_code":{"code_sha256":"b9edb542ddeedb32"}},{"arxiv_id":"2403.19600","paper":"/paper/enhance-image-classification-via-inter-class","title":"Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhicaiwww/diff-mix","path":"downstream_tasks/losses.py","file_url":"https://github.com/zhicaiwww/diff-mix/blob/HEAD/downstream_tasks/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8770a72903df63a4","mcp_get_code":{"code_sha256":"8770a72903df63a4"}},{"arxiv_id":"2402.05453","paper":"/paper/mitigating-privacy-risk-in-membership","title":"Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-stat-sustech/convexconcaveloss","path":"source/defenses/membership_inference/loss_function.py","file_url":"https://github.com/ml-stat-sustech/convexconcaveloss/blob/HEAD/source/defenses/membership_inference/loss_function.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6c954a2933604c5","mcp_get_code":{"code_sha256":"b6c954a2933604c5"}},{"arxiv_id":"2312.00083","paper":"/paper/bam-detr-boundary-aligned-moment-detection","title":"BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videos","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Pilhyeon/BAM-DETR","path":"bam_detr/misc.py","file_url":"https://github.com/Pilhyeon/BAM-DETR/blob/HEAD/bam_detr/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"fd215ad1001ab061","mcp_get_code":{"code_sha256":"fd215ad1001ab061"}},{"arxiv_id":"2311.03236","paper":"/paper/out-of-distribution-detection-learning-with","title":"Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources","date":"2023-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlr-group/atol","path":"utils/losses.py","file_url":"https://github.com/tmlr-group/atol/blob/HEAD/utils/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2310.02861","paper":"/paper/rayleigh-quotient-graph-neural-networks-for","title":"Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection","date":"2023-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xydong127/rqgnn","path":"lossfunc.py","file_url":"https://github.com/xydong127/rqgnn/blob/HEAD/lossfunc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"128bf43313aa9f8a","mcp_get_code":{"code_sha256":"128bf43313aa9f8a"}},{"arxiv_id":"2309.10019","paper":"/paper/parameter-efficient-long-tailed-recognition","title":"Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts","date":"2023-09-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shijxcs/LIFT","path":"utils/losses.py","file_url":"https://github.com/shijxcs/LIFT/blob/HEAD/utils/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2308.14575","paper":"/paper/referring-image-segmentation-using-text","title":"Referring Image Segmentation Using Text Supervision","date":"2023-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fawnliu/tris","path":"model/model_stage1.py","file_url":"https://github.com/fawnliu/tris/blob/HEAD/model/model_stage1.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb29cf6c7fa94676","mcp_get_code":{"code_sha256":"cb29cf6c7fa94676"}},{"arxiv_id":"2308.14181","paper":"/paper/topological-augmentation-for-class-imbalanced","title":"Class-Imbalanced Graph Learning without Class Rebalancing","date":"2023-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhiningliu1998/bat","path":"baselines/renode.py","file_url":"https://github.com/zhiningliu1998/bat/blob/HEAD/baselines/renode.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6ccd040c4ef8720b","mcp_get_code":{"code_sha256":"6ccd040c4ef8720b"}},{"arxiv_id":"2308.09922","paper":"/paper/mdcs-more-diverse-experts-with-consistency","title":"MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition","date":"2023-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fistyee/mdcs","path":"model/loss.py","file_url":"https://github.com/fistyee/mdcs/blob/HEAD/model/loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2308.02000","paper":"/paper/on-the-transition-from-neural-representation","title":"On the Transition from Neural Representation to Symbolic Knowledge","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chengjunyan1/TDL","path":"TAE.py","file_url":"https://github.com/chengjunyan1/TDL/blob/HEAD/TAE.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1900ff852f0930d","mcp_get_code":{"code_sha256":"c1900ff852f0930d"}},{"arxiv_id":"2306.04621","paper":"/paper/align-distill-and-augment-everything-all-at","title":"Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper Calibration","date":"2023-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"emasa/balpoe-calibratedlt","path":"model/loss.py","file_url":"https://github.com/emasa/balpoe-calibratedlt/blob/HEAD/model/loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2305.16133","paper":"/paper/ovo-open-vocabulary-occupancy","title":"OVO: Open-Vocabulary Occupancy","date":"2023-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dzcgaara/OVO","path":"ovo/loss/mask_matching_loss.py","file_url":"https://github.com/dzcgaara/OVO/blob/HEAD/ovo/loss/mask_matching_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3a8c2b6651437097","mcp_get_code":{"code_sha256":"3a8c2b6651437097"}},{"arxiv_id":"2305.11733","paper":"/paper/long-tailed-visual-recognition-via-gaussian-1","title":"Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"1b3f82e6c53766e7","mcp_get_code":{"code_sha256":"1b3f82e6c53766e7"}},{"arxiv_id":"2305.10772","paper":"/paper/feature-balanced-loss-for-long-tailed-visual-1","title":"Feature-Balanced Loss for Long-Tailed Visual Recognition","date":"2023-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juyongjiang/fbl","path":"loss.py","file_url":"https://github.com/juyongjiang/fbl/blob/HEAD/loss.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1b3f82e6c53766e7","mcp_get_code":{"code_sha256":"1b3f82e6c53766e7"}},{"arxiv_id":"2304.09426","paper":"/paper/decoupled-training-for-long-tailed","title":"Decoupled Training for Long-Tailed Classification With Stochastic Representations","date":"2023-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kaidic/LDAM-DRW","path":"losses.py","file_url":"https://github.com/kaidic/LDAM-DRW/blob/HEAD/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2304.01279","paper":"/paper/long-tailed-visual-recognition-via-self","title":"Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation","date":"2023-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"1b3f82e6c53766e7","mcp_get_code":{"code_sha256":"1b3f82e6c53766e7"}},{"arxiv_id":"2303.09870","paper":"/paper/tesla-test-time-self-learning-with-automatic","title":"TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"devavratTomar/TeSLA","path":"losses/seg_losses.py","file_url":"https://github.com/devavratTomar/TeSLA/blob/HEAD/losses/seg_losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0748e29a637d95ec","mcp_get_code":{"code_sha256":"0748e29a637d95ec"}},{"arxiv_id":"2302.08872","paper":"/paper/revisiting-adversarial-training-for-the-worst","title":"Revisiting adversarial training for the worst-performing class","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lions-epfl/class-focused-online-learning-code","path":"cfol/focal_loss.py","file_url":"https://github.com/lions-epfl/class-focused-online-learning-code/blob/HEAD/cfol/focal_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4bebda9ddfdffe0a","mcp_get_code":{"code_sha256":"4bebda9ddfdffe0a"}},{"arxiv_id":"2210.11065","paper":"/paper/movieclip-visual-scene-recognition-in-movies","title":"MovieCLIP: Visual Scene Recognition in Movies","date":"2022-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"usc-sail/mica-MovieCLIP","path":"losses/loss_functions.py","file_url":"https://github.com/usc-sail/mica-MovieCLIP/blob/HEAD/losses/loss_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4bebda9ddfdffe0a","mcp_get_code":{"code_sha256":"4bebda9ddfdffe0a"}},{"arxiv_id":"2206.11251","paper":"/paper/behavior-transformers-cloning-k-modes-with","title":"Behavior Transformers: Cloning $k$ modes with one stone","date":"2022-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"notmahi/bet","path":"models/libraries/loss_fn.py","file_url":"https://github.com/notmahi/bet/blob/HEAD/models/libraries/loss_fn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f840d01c761c3844","mcp_get_code":{"code_sha256":"f840d01c761c3844"}},{"arxiv_id":"2112.01527","paper":"/paper/masked-attention-mask-transformer-for","title":"Masked-attention Mask Transformer for Universal Image Segmentation","date":"2021-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DdeGeus/Mask2Former-IBS","path":"mask2former/modeling/criterion.py","file_url":"https://github.com/DdeGeus/Mask2Former-IBS/blob/HEAD/mask2former/modeling/criterion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2bb4c1887f16e1ba","mcp_get_code":{"code_sha256":"2bb4c1887f16e1ba"}},{"arxiv_id":"2110.04099","paper":"/paper/topology-imbalance-learning-for-semi","title":"Topology-Imbalance Learning for Semi-Supervised Node Classification","date":"2021-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"victorchen96/renode","path":"transductive/imb_loss.py","file_url":"https://github.com/victorchen96/renode/blob/HEAD/transductive/imb_loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6ccd040c4ef8720b","mcp_get_code":{"code_sha256":"6ccd040c4ef8720b"}},{"arxiv_id":"2107.09249","paper":"/paper/test-agnostic-long-tailed-recognition-by-test","title":"Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition","date":"2021-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vanint/TADE-AgnosticLT","path":"model/loss.py","file_url":"https://github.com/Vanint/TADE-AgnosticLT/blob/HEAD/model/loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"2105.05537","paper":"/paper/swin-unet-unet-like-pure-transformer-for","title":"Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation","date":"2021-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"L-A-Sandhu/Swin-Unet-","path":"src/swin_transformer/AUC_LOSS.py","file_url":"https://github.com/L-A-Sandhu/Swin-Unet-/blob/HEAD/src/swin_transformer/AUC_LOSS.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dc39ed696312e230","mcp_get_code":{"code_sha256":"dc39ed696312e230"}},{"arxiv_id":"2104.03501","paper":"/paper/deepi2p-image-to-point-cloud-registration-via","title":"DeepI2P: Image-to-Point Cloud Registration via Deep Classification","date":"2021-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lijx10/DeepI2P","path":"models/focal_loss.py","file_url":"https://github.com/lijx10/DeepI2P/blob/HEAD/models/focal_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36d967d4eb8179d5","mcp_get_code":{"code_sha256":"36d967d4eb8179d5"}},{"arxiv_id":"2103.04527","paper":"/paper/one-shot-medical-landmark-detection","title":"One-Shot Medical Landmark Detection","date":"2021-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Curli-quan/oneshot-medical-landmark","path":"scripts/self_train.py","file_url":"https://github.com/Curli-quan/oneshot-medical-landmark/blob/HEAD/scripts/self_train.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"eb19c575ed458088","mcp_get_code":{"code_sha256":"eb19c575ed458088"}},{"arxiv_id":"2011.01776","paper":"/paper/leveraging-activity-recognition-to-enable","title":"Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data","date":"2020-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mvrjustid/IMWUT-Hierarchical-HAR-PBD","path":"code/utils.py","file_url":"https://github.com/Mvrjustid/IMWUT-Hierarchical-HAR-PBD/blob/HEAD/code/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6853f6c77c17fc20","mcp_get_code":{"code_sha256":"6853f6c77c17fc20"}},{"arxiv_id":"2010.12035","paper":"/paper/keep-your-eyes-on-the-lane-attention-guided","title":"Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucastabelini/LaneATT","path":"lib/focal_loss.py","file_url":"https://github.com/lucastabelini/LaneATT/blob/HEAD/lib/focal_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e1fffb767af25e4","mcp_get_code":{"code_sha256":"2e1fffb767af25e4"}},{"arxiv_id":"2006.05683","paper":"/paper/tubetk-adopting-tubes-to-track-multi-object-1","title":"TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model","date":"2020-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BoPang1996/TubeTK","path":"network/focal_loss.py","file_url":"https://github.com/BoPang1996/TubeTK/blob/HEAD/network/focal_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0d548c0e307771fa","mcp_get_code":{"code_sha256":"0d548c0e307771fa"}},{"arxiv_id":"2006.05077","paper":"/paper/sekd-self-evolving-keypoint-detection-and","title":"SEKD: Self-Evolving Keypoint Detection and Description","date":"2020-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aliyun/Self-Evolving-Keypoint-Demo","path":"loss/loss_focal.py","file_url":"https://github.com/aliyun/Self-Evolving-Keypoint-Demo/blob/HEAD/loss/loss_focal.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"deb13554676659ec","mcp_get_code":{"code_sha256":"deb13554676659ec"}},{"arxiv_id":"2005.08104","paper":"/paper/single-stage-semantic-segmentation-from-image","title":"Single-Stage Semantic Segmentation from Image Labels","date":"2020-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/1-stage-wseg","path":"models/stage_net.py","file_url":"https://github.com/visinf/1-stage-wseg/blob/HEAD/models/stage_net.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cb29cf6c7fa94676","mcp_get_code":{"code_sha256":"cb29cf6c7fa94676"}},{"arxiv_id":"2004.08790","paper":"/paper/unet-3-a-full-scale-connected-unet-for","title":"UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation","date":"2020-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hamidriasat/UNet-3-Plus","path":"losses/loss.py","file_url":"https://github.com/hamidriasat/UNet-3-Plus/blob/HEAD/losses/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7b23f6df494f23b1","mcp_get_code":{"code_sha256":"7b23f6df494f23b1"}},{"arxiv_id":"2004.05405","paper":"/paper/unveiling-covid-19-from-chest-x-ray-with-deep","title":"Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data","date":"2020-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EIDOSlab/unveiling-covid19-from-cxr","path":"train-covid-classifier.py","file_url":"https://github.com/EIDOSlab/unveiling-covid19-from-cxr/blob/HEAD/train-covid-classifier.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"04198a019f317705","mcp_get_code":{"code_sha256":"04198a019f317705"}},{"arxiv_id":"2003.12464","paper":"/paper/end-to-end-autonomous-driving-perception-with","title":"End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning","date":"2020-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjy1992/detect-loc-map","path":"perception_driving/networks/sequential_latent_pixor_network.py","file_url":"https://github.com/cjy1992/detect-loc-map/blob/HEAD/perception_driving/networks/sequential_latent_pixor_network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"67e87c930497a29b","mcp_get_code":{"code_sha256":"67e87c930497a29b"}},{"arxiv_id":"2003.09163","paper":"/paper/detection-in-crowded-scenes-one-proposal","title":"Detection in Crowded Scenes: One Proposal, Multiple Predictions","date":"2020-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Purkialo/CrowdDet","path":"lib/det_oprs/loss_opr.py","file_url":"https://github.com/Purkialo/CrowdDet/blob/HEAD/lib/det_oprs/loss_opr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f2466bea04ccb88b","mcp_get_code":{"code_sha256":"f2466bea04ccb88b"}},{"arxiv_id":"1911.08299","paper":"/paper/learning-modulated-loss-for-rotated-object","title":"Learning Modulated Loss for Rotated Object Detection","date":"2019-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mrqianduoduo/RSDet-8P-4R","path":"libs/losses/losses.py","file_url":"https://github.com/Mrqianduoduo/RSDet-8P-4R/blob/HEAD/libs/losses/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e57f95591345ae49","mcp_get_code":{"code_sha256":"e57f95591345ae49"}},{"arxiv_id":"1909.02466","paper":"/paper/freeanchor-learning-to-match-anchors-for","title":"FreeAnchor: Learning to Match Anchors for Visual Object Detection","date":"2019-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangxiaosong18/FreeAnchor","path":"maskrcnn_benchmark/modeling/rpn/free_anchor_loss.py","file_url":"https://github.com/zhangxiaosong18/FreeAnchor/blob/HEAD/maskrcnn_benchmark/modeling/rpn/free_anchor_loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4392a86bb773ddd","mcp_get_code":{"code_sha256":"b4392a86bb773ddd"}},{"arxiv_id":"1906.07413","paper":"/paper/learning-imbalanced-datasets-with-label","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","date":"2019-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ihaeyong/maximum-margin-ldam","path":"losses.py","file_url":"https://github.com/ihaeyong/maximum-margin-ldam/blob/HEAD/losses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuannianz/keras-CenterNet","path":"losses.py","file_url":"https://github.com/xuannianz/keras-CenterNet/blob/HEAD/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ca7ad1a4c7d4631d","mcp_get_code":{"code_sha256":"ca7ad1a4c7d4631d"}},{"arxiv_id":"1904.01355","paper":"/paper/fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"srihari-humbarwadi/tensorflow_fcos","path":"tensorflow_fcos/models/fcos/losses.py","file_url":"https://github.com/srihari-humbarwadi/tensorflow_fcos/blob/HEAD/tensorflow_fcos/models/fcos/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fce50f2fda34fc22","mcp_get_code":{"code_sha256":"fce50f2fda34fc22"}},{"arxiv_id":"1903.08548","paper":"/paper/learning-convolutional-transforms-for-lossy","title":"Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression","date":"2019-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mauriceqch/pcc_geo_cnn","path":"src/focal_loss.py","file_url":"https://github.com/mauriceqch/pcc_geo_cnn/blob/HEAD/src/focal_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"427c81f065a9770a","mcp_get_code":{"code_sha256":"427c81f065a9770a"}},{"arxiv_id":"1902.08570","paper":"/paper/particlenet-jet-tagging-via-particle-clouds","title":"ParticleNet: Jet Tagging via Particle Clouds","date":"2019-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hqucms/weaver-core","path":"weaver/nn/loss/focal.py","file_url":"https://github.com/hqucms/weaver-core/blob/HEAD/weaver/nn/loss/focal.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"22cdb78ad8aed2f6","mcp_get_code":{"code_sha256":"22cdb78ad8aed2f6"}},{"arxiv_id":"1901.05555","paper":"/paper/class-balanced-loss-based-on-effective-number","title":"Class-Balanced Loss Based on Effective Number of Samples","date":"2019-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vandit15/Class-balanced-loss-pytorch","path":"class_balanced_loss.py","file_url":"https://github.com/vandit15/Class-balanced-loss-pytorch/blob/HEAD/class_balanced_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c32ccdb4e101c470","mcp_get_code":{"code_sha256":"c32ccdb4e101c470"}},{"arxiv_id":"1807.03748","paper":"/paper/representation-learning-with-contrastive","title":"Representation Learning with Contrastive Predictive Coding","date":"2018-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ex4sperans/freesound-classification","path":"networks/losses.py","file_url":"https://github.com/ex4sperans/freesound-classification/blob/HEAD/networks/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"25b22350f74af590","mcp_get_code":{"code_sha256":"25b22350f74af590"}},{"arxiv_id":"1804.09337","paper":"/paper/learning-a-discriminative-feature-network-for","title":"Learning a Discriminative Feature Network for Semantic Segmentation","date":"2018-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuhuiMa/DFN-tensorflow","path":"losses.py","file_url":"https://github.com/YuhuiMa/DFN-tensorflow/blob/HEAD/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8acbd47e113d2681","mcp_get_code":{"code_sha256":"8acbd47e113d2681"}},{"arxiv_id":"1708.02002","paper":"/paper/focal-loss-for-dense-object-detection","title":"Focal Loss for Dense Object Detection","date":"2017-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daveboat/pytorch_focal_loss","path":"focalloss.py","file_url":"https://github.com/daveboat/pytorch_focal_loss/blob/HEAD/focalloss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f5a902a2510561a","mcp_get_code":{"code_sha256":"6f5a902a2510561a"}},{"arxiv_id":"1708.02002","paper":"/paper/focal-loss-for-dense-object-detection","title":"Focal Loss for Dense Object Detection","date":"2017-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AdeelH/pytorch-multi-class-focal-loss","path":"focal_loss.py","file_url":"https://github.com/AdeelH/pytorch-multi-class-focal-loss/blob/HEAD/focal_loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa6467d64911f0e4","mcp_get_code":{"code_sha256":"aa6467d64911f0e4"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jo-dsa/SemanticSeg","path":"src/model.py","file_url":"https://github.com/Jo-dsa/SemanticSeg/blob/HEAD/src/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3c8319d0c4370678","mcp_get_code":{"code_sha256":"3c8319d0c4370678"}},{"arxiv_id":"openreview_mJr1VDMOf2","paper":null,"title":"arXiv:openreview_mJr1VDMOf2","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Nebulae411/DGG-HMR","path":"models/criterion.py","file_url":"https://github.com/Nebulae411/DGG-HMR/blob/HEAD/models/criterion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a7e2bec8d2a4e3cf","mcp_get_code":{"code_sha256":"a7e2bec8d2a4e3cf"}},{"arxiv_id":"Aimar_Balanced_Product_of_Calibrated_Experts_for_Long-Tailed_Recognition_CVPR_2023_paper","paper":null,"title":"arXiv:Aimar_Balanced_Product_of_Calibrated_Experts_for_Long-Tailed_Recognition_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"emasa/BalPoE-CalibratedLT","path":"model/loss.py","file_url":"https://github.com/emasa/BalPoE-CalibratedLT/blob/HEAD/model/loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dcf06ba28983266","mcp_get_code":{"code_sha256":"4dcf06ba28983266"}}]}