{"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/vgg19","entry":"vgg19","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":10,"n_papers_ran":2,"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":11,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":13,"n_places_pointer_only":8,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":1,"unverified":7},"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":"2506.20548","paper":"/paper/pay-less-attention-to-deceptive-artifacts","title":"Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks","date":"2025-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manyilee/plada","path":"models/vgg.py","file_url":"https://github.com/manyilee/plada/blob/HEAD/models/vgg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36aa35f7d5c690bd","mcp_get_code":{"code_sha256":"36aa35f7d5c690bd"}},{"arxiv_id":"2406.06526","paper":"/paper/gaussiancity-generative-gaussian-splatting","title":"GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation","date":"2024-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzxie/GaussianCity","path":"losses/perceptual.py","file_url":"https://github.com/hzxie/GaussianCity/blob/HEAD/losses/perceptual.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"396918e041c75412","mcp_get_code":{"code_sha256":"396918e041c75412"}},{"arxiv_id":"2403.07968","paper":"/paper/do-deep-neural-network-solutions-form-a-star","title":"Do Deep Neural Network Solutions Form a Star Domain?","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aktsonthalia/starlight","path":"models/vgg.py","file_url":"https://github.com/aktsonthalia/starlight/blob/HEAD/models/vgg.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f75033e651281e6a","mcp_get_code":{"code_sha256":"f75033e651281e6a"}},{"arxiv_id":"2309.00610","paper":"/paper/citydreamer-compositional-generative-model-of","title":"CityDreamer: Compositional Generative Model of Unbounded 3D Cities","date":"2023-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzxie/CityDreamer","path":"losses/perceptual.py","file_url":"https://github.com/hzxie/CityDreamer/blob/HEAD/losses/perceptual.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"396918e041c75412","mcp_get_code":{"code_sha256":"396918e041c75412"}},{"arxiv_id":"2302.10174","paper":"/paper/towards-universal-fake-image-detectors-that","title":"Towards Universal Fake Image Detectors that Generalize Across Generative Models","date":"2023-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuheng-li/universalfakedetect","path":"models/vgg.py","file_url":"https://github.com/yuheng-li/universalfakedetect/blob/HEAD/models/vgg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"36aa35f7d5c690bd","mcp_get_code":{"code_sha256":"36aa35f7d5c690bd"}},{"arxiv_id":"2203.16172","paper":"/paper/self-distillation-from-the-last-mini-batch","title":"Self-Distillation from the Last Mini-Batch for Consistency Regularization","date":"2022-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Meta-knowledge-Lab/DLB","path":"models/vgg.py","file_url":"https://github.com/Meta-knowledge-Lab/DLB/blob/HEAD/models/vgg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f38a8b4c76fc2cb","mcp_get_code":{"code_sha256":"9f38a8b4c76fc2cb"}},{"arxiv_id":"2106.09309","paper":"/paper/layer-folding-neural-network-depth-reduction","title":"Layer Folding: Neural Network Depth Reduction using Activation Linearization","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LayerFolding/Layer-Folding","path":"VGG_Cifar10_postfold.py","file_url":"https://github.com/LayerFolding/Layer-Folding/blob/HEAD/VGG_Cifar10_postfold.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"1ad77b38966daedd","mcp_get_code":{"code_sha256":"1ad77b38966daedd"}},{"arxiv_id":"2106.09309","paper":"/paper/layer-folding-neural-network-depth-reduction","title":"Layer Folding: Neural Network Depth Reduction using Activation Linearization","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LayerFolding/Layer-Folding","path":"VGG_Cifar10_prefold.py","file_url":"https://github.com/LayerFolding/Layer-Folding/blob/HEAD/VGG_Cifar10_prefold.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"50714c640ea17fb6","mcp_get_code":{"code_sha256":"50714c640ea17fb6"}},{"arxiv_id":"2106.06916","paper":"/paper/non-transferable-learning-a-new-approach-for","title":"Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"conditionWang/NTL","path":"src/ntl/ntl_cifar_stl.py","file_url":"https://github.com/conditionWang/NTL/blob/HEAD/src/ntl/ntl_cifar_stl.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f0336aa7f62b0168","mcp_get_code":{"code_sha256":"f0336aa7f62b0168"}},{"arxiv_id":"2106.06916","paper":"/paper/non-transferable-learning-a-new-approach-for","title":"Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"conditionWang/NTL","path":"src/ntl/ntl_visda.py","file_url":"https://github.com/conditionWang/NTL/blob/HEAD/src/ntl/ntl_visda.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"928972c127cac438","mcp_get_code":{"code_sha256":"928972c127cac438"}},{"arxiv_id":"2106.06916","paper":"/paper/non-transferable-learning-a-new-approach-for","title":"Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"conditionWang/NTL","path":"src/ntl/ntl_cifar_stl.py","file_url":"https://github.com/conditionWang/NTL/blob/HEAD/src/ntl/ntl_cifar_stl.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"95c47c389db597ab","mcp_get_code":{"code_sha256":"95c47c389db597ab"}},{"arxiv_id":"1905.03670","paper":"/paper/190503670","title":"S4L: Self-Supervised Semi-Supervised Learning","date":"2019-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/s4l","path":"models/vggnet.py","file_url":"https://github.com/google-research/s4l/blob/HEAD/models/vggnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"f546a29cc665024e","mcp_get_code":{"code_sha256":"f546a29cc665024e"}},{"arxiv_id":"1901.09005","paper":"/paper/revisiting-self-supervised-visual","title":"Revisiting Self-Supervised Visual Representation Learning","date":"2019-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rickyHong/Puzzle-tensorflow-latest-repl","path":"models/vggnet.py","file_url":"https://github.com/rickyHong/Puzzle-tensorflow-latest-repl/blob/HEAD/models/vggnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"5f17db2cd3b2b06d","mcp_get_code":{"code_sha256":"5f17db2cd3b2b06d"}}]}