{"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/noise-estimation-loss","entry":"noise_estimation_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":19,"n_papers_ran":15,"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":1,"n_samples_fingerprinted":0,"n_places":19,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":4},"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":"2412.05926","paper":"/paper/bidm-pushing-the-limit-of-quantization-for","title":"BiDM: Pushing the Limit of Quantization for Diffusion Models","date":"2024-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xingyu-zheng/bidm","path":"bidm-cifar/functions/losses.py","file_url":"https://github.com/xingyu-zheng/bidm/blob/HEAD/bidm-cifar/functions/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e7829cb162499d04","mcp_get_code":{"code_sha256":"e7829cb162499d04"}},{"arxiv_id":"2409.19732","paper":"/paper/unified-gradient-based-machine-unlearning","title":"Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement","date":"2024-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clear-nus/selective-amnesia","path":"ddpm/functions/losses.py","file_url":"https://github.com/clear-nus/selective-amnesia/blob/HEAD/ddpm/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2407.19547","paper":"/paper/temporal-feature-matters-a-framework-for","title":"Temporal Feature Matters: A Framework for Diffusion Model Quantization","date":"2024-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"modeltc/tfmq-dm","path":"ddim/functions/losses.py","file_url":"https://github.com/modeltc/tfmq-dm/blob/HEAD/ddim/functions/losses.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":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2406.12837","paper":"/paper/layermerge-neural-network-depth-compression","title":"LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snu-mllab/LayerMerge","path":"layer_merge/ddpm_trainer.py","file_url":"https://github.com/snu-mllab/LayerMerge/blob/HEAD/layer_merge/ddpm_trainer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2403.19140","paper":"/paper/qncd-quantization-noise-correction-for","title":"QNCD: Quantization Noise Correction for Diffusion Models","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huanpengchu/qncd","path":"ddim/functions/losses.py","file_url":"https://github.com/huanpengchu/qncd/blob/HEAD/ddim/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2402.11846","paper":"/paper/unlearncanvas-a-stylized-image-dataset-to","title":"UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JingWu321/EraseDiff","path":"ddpm/functions/losses.py","file_url":"https://github.com/JingWu321/EraseDiff/blob/HEAD/ddpm/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2401.05779","paper":"/paper/erasediff-erasing-data-influence-in-diffusion","title":"Erasing Undesirable Influence in Diffusion Models","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingwu321/erasediff","path":"ddpm/functions/losses.py","file_url":"https://github.com/jingwu321/erasediff/blob/HEAD/ddpm/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2312.12030","paper":"/paper/towards-accurate-guided-diffusion-sampling","title":"Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method","date":"2023-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanshuyan/adjointdpm","path":"ddpm_and_guided-diffusion/functions/losses.py","file_url":"https://github.com/hanshuyan/adjointdpm/blob/HEAD/ddpm_and_guided-diffusion/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2311.11600","paper":"/paper/deep-equilibrium-diffusion-restoration-with","title":"Deep Equilibrium Diffusion Restoration with Parallel Sampling","date":"2023-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"caojiezhang/deqir","path":"functions/losses.py","file_url":"https://github.com/caojiezhang/deqir/blob/HEAD/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2311.01226","paper":"/paper/optimal-transport-guided-conditional-score","title":"Optimal Transport-Guided Conditional Score-Based Diffusion Models","date":"2023-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XJTU-XGU/OTCS","path":"functions/losses.py","file_url":"https://github.com/XJTU-XGU/OTCS/blob/HEAD/functions/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1445873866afd900","mcp_get_code":{"code_sha256":"1445873866afd900"}},{"arxiv_id":"2310.11142","paper":"/paper/bayesdiff-estimating-pixel-wise-uncertainty","title":"BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference","date":"2023-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karrykkk/BayesDiff","path":"ddpm_and_guided/functions/losses.py","file_url":"https://github.com/karrykkk/BayesDiff/blob/HEAD/ddpm_and_guided/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2307.14659","paper":"/paper/lldiffusion-learning-degradation","title":"LLDiffusion: Learning Degradation Representations in Diffusion Models for Low-Light Image Enhancement","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taowangzj/lldiffusion","path":"models/losses.py","file_url":"https://github.com/taowangzj/lldiffusion/blob/HEAD/models/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bbe1a627c59bd3d3","mcp_get_code":{"code_sha256":"bbe1a627c59bd3d3"}},{"arxiv_id":"2302.03262","paper":"/paper/membership-inference-attacks-against-1","title":"Membership Inference Attacks against Diffusion Models","date":"2023-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fseclab-osaka/mia-diffusion","path":"ddim/functions/losses.py","file_url":"https://github.com/fseclab-osaka/mia-diffusion/blob/HEAD/ddim/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2301.07969","paper":"/paper/fast-inference-in-denoising-diffusion-models","title":"Fast Inference in Denoising Diffusion Models via MMD Finetuning","date":"2023-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"diegovalsesia/mmd-ddm","path":"functions/losses.py","file_url":"https://github.com/diegovalsesia/mmd-ddm/blob/HEAD/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2211.14680","paper":"/paper/a-physics-informed-diffusion-model-for-high","title":"A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction","date":"2022-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BaratiLab/Diffusion-based-Fluid-Super-resolution","path":"train_ddpm/functions/losses.py","file_url":"https://github.com/BaratiLab/Diffusion-based-Fluid-Super-resolution/blob/HEAD/train_ddpm/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2210.11633","paper":"/paper/graphically-structured-diffusion-models","title":"Graphically Structured Diffusion Models","date":"2022-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"plai-group/gsdm","path":"functions/losses.py","file_url":"https://github.com/plai-group/gsdm/blob/HEAD/functions/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c639cf282cbd3993","mcp_get_code":{"code_sha256":"c639cf282cbd3993"}},{"arxiv_id":"2206.02262","paper":"/paper/diffusion-gan-training-gans-with-diffusion","title":"Diffusion-GAN: Training GANs with Diffusion","date":"2022-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jegzheng/truncated-diffusion-probabilistic-models","path":"functions/losses.py","file_url":"https://github.com/jegzheng/truncated-diffusion-probabilistic-models/blob/HEAD/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"2106.06819","paper":"/paper/d2c-diffusion-denoising-models-for-few-shot","title":"D2C: Diffusion-Denoising Models for Few-shot Conditional Generation","date":"2021-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiamings/d2c","path":"d2c/diffusion/functions/losses.py","file_url":"https://github.com/jiamings/d2c/blob/HEAD/d2c/diffusion/functions/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}},{"arxiv_id":"Huang_TFMQ-DM_Temporal_Feature_Maintenance_Quantization_for_Diffusion_Models_CVPR_2024_paper","paper":null,"title":"arXiv:Huang_TFMQ-DM_Temporal_Feature_Maintenance_Quantization_for_Diffusion_Models_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ModelTC/TFMQ-DM","path":"ddim/functions/losses.py","file_url":"https://github.com/ModelTC/TFMQ-DM/blob/HEAD/ddim/functions/losses.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":"36a45cceb1209d2f","mcp_get_code":{"code_sha256":"36a45cceb1209d2f"}}]}