{"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/calculate-output-image-size","entry":"calculate_output_image_size","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":9,"n_papers_ran":9,"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":3,"n_samples_ran":3,"n_samples_fingerprinted":1,"n_places":9,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"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":"2505.24254","paper":"/paper/rethinking-continual-learning-with","title":"Rethinking Continual Learning with Progressive Neural Collapse","date":"2025-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Continue-Edge-AI-Lab/ProNC","path":"backbone/EfficientNet.py","file_url":"https://github.com/Continue-Edge-AI-Lab/ProNC/blob/HEAD/backbone/EfficientNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e7efa6d1976111a","mcp_get_code":{"code_sha256":"7e7efa6d1976111a"}},{"arxiv_id":"2410.06645","paper":"/paper/continual-learning-in-the-frequency-domain","title":"Continual Learning in the Frequency Domain","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EMLS-ICTCAS/CLFD","path":"backbone/EfficientNet.py","file_url":"https://github.com/EMLS-ICTCAS/CLFD/blob/HEAD/backbone/EfficientNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e7efa6d1976111a","mcp_get_code":{"code_sha256":"7e7efa6d1976111a"}},{"arxiv_id":"2407.15793","paper":"/paper/clip-with-generative-latent-replay-a-strong","title":"CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aimagelab/mammoth","path":"backbone/EfficientNet.py","file_url":"https://github.com/aimagelab/mammoth/blob/HEAD/backbone/EfficientNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e7efa6d1976111a","mcp_get_code":{"code_sha256":"7e7efa6d1976111a"}},{"arxiv_id":"2404.04002","paper":"/paper/continual-learning-with-weight-interpolation","title":"Continual Learning with Weight Interpolation","date":"2024-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jedrzejkozal/weight-interpolation-cl","path":"backbone/EfficientNet.py","file_url":"https://github.com/jedrzejkozal/weight-interpolation-cl/blob/HEAD/backbone/EfficientNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e7efa6d1976111a","mcp_get_code":{"code_sha256":"7e7efa6d1976111a"}},{"arxiv_id":"2403.13249","paper":"/paper/a-unified-and-general-framework-for-continual","title":"A Unified and General Framework for Continual Learning","date":"2024-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joey-wang123/CL-refresh-learning","path":"backbone/EfficientNet.py","file_url":"https://github.com/joey-wang123/CL-refresh-learning/blob/HEAD/backbone/EfficientNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"79484137b284aee1","mcp_get_code":{"code_sha256":"79484137b284aee1"}},{"arxiv_id":"2403.01786","paper":"/paper/exposing-the-deception-uncovering-more","title":"Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qingyuliu/exposing-the-deception","path":"models/MI_Net.py","file_url":"https://github.com/qingyuliu/exposing-the-deception/blob/HEAD/models/MI_Net.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1463b46fb531ddac","mcp_get_code":{"code_sha256":"1463b46fb531ddac"}},{"arxiv_id":"2310.02206","paper":"/paper/chunking-forgetting-matters-in-continual","title":"Chunking: Continual Learning is not just about Distribution Shift","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tlee43/chunking-setting","path":"backbone/EfficientNet.py","file_url":"https://github.com/tlee43/chunking-setting/blob/HEAD/backbone/EfficientNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e7efa6d1976111a","mcp_get_code":{"code_sha256":"7e7efa6d1976111a"}},{"arxiv_id":"2304.10177","paper":"/paper/regularizing-second-order-influences-for","title":"Regularizing Second-Order Influences for Continual Learning","date":"2023-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feifeiobama/InfluenceCL","path":"backbone/EfficientNet.py","file_url":"https://github.com/feifeiobama/InfluenceCL/blob/HEAD/backbone/EfficientNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e7efa6d1976111a","mcp_get_code":{"code_sha256":"7e7efa6d1976111a"}},{"arxiv_id":"aaai_32254","paper":null,"title":"arXiv:aaai_32254","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HongsinLee/FLAIR","path":"backbone/EfficientNet.py","file_url":"https://github.com/HongsinLee/FLAIR/blob/HEAD/backbone/EfficientNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e7efa6d1976111a","mcp_get_code":{"code_sha256":"7e7efa6d1976111a"}}]}