{"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/densenet","entry":"DenseNet","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":5,"n_papers_ran":5,"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":5,"n_samples_ran":5,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":5,"unverified":0},"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":"2207.02606","paper":"/paper/densehybrid-hybrid-anomaly-detection-for","title":"DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition","date":"2022-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"matejgrcic/DenseHybrid","path":"models/ladder_densenet.py","file_url":"https://github.com/matejgrcic/DenseHybrid/blob/HEAD/models/ladder_densenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-2.0","inline_ok":false,"code_sha256_prefix":"82fa155ecf7dd6ea","mcp_get_code":{"code_sha256":"82fa155ecf7dd6ea"}},{"arxiv_id":"2112.06753","paper":"/paper/finrl-meta-a-universe-of-near-real-market","title":"FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance","date":"2021-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai4finance-foundation/finrl-meta","path":"meta/env_future_trading/wt4elegantrl/elegantrl/agent.py","file_url":"https://github.com/ai4finance-foundation/finrl-meta/blob/HEAD/meta/env_future_trading/wt4elegantrl/elegantrl/agent.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6cf5ceb24c4e37a2","mcp_get_code":{"code_sha256":"6cf5ceb24c4e37a2"}},{"arxiv_id":"2104.01231","paper":"/paper/misclassification-aware-gaussian-smoothing","title":"Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration","date":"2021-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theot1/dign","path":"DiGN.py","file_url":"https://github.com/theot1/dign/blob/HEAD/DiGN.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"757ed71343ce4c3b","mcp_get_code":{"code_sha256":"757ed71343ce4c3b"}},{"arxiv_id":"2010.08001","paper":"/paper/maximum-entropy-adversarial-data-augmentation","title":"Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness","date":"2020-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"garyzhao/ME-ADA","path":"model_cifar.py","file_url":"https://github.com/garyzhao/ME-ADA/blob/HEAD/model_cifar.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"1e6effa8af67f1f1","mcp_get_code":{"code_sha256":"1e6effa8af67f1f1"}},{"arxiv_id":"2005.12872","paper":"/paper/end-to-end-object-detection-with-transformers","title":"End-to-End Object Detection with Transformers","date":"2020-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clive819/Modified-DETR","path":"models/detr.py","file_url":"https://github.com/clive819/Modified-DETR/blob/HEAD/models/detr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a3f28612aea2e52","mcp_get_code":{"code_sha256":"2a3f28612aea2e52"}}]}