{"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/adjust-dynamic-range","entry":"adjust_dynamic_range","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":7,"n_papers_ran":3,"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":6,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":11,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":5},"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":"2112.07804","paper":"/paper/tackling-the-generative-learning-trilemma-1","title":"Tackling the Generative Learning Trilemma with Denoising Diffusion GANs","date":"2021-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taki0112/denoising-diffusion-gan-Tensorflow","path":"src/utils.py","file_url":"https://github.com/taki0112/denoising-diffusion-gan-Tensorflow/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"580ee01331365cc1","mcp_get_code":{"code_sha256":"580ee01331365cc1"}},{"arxiv_id":"1909.07083","paper":"/paper/controllable-text-to-image-generation","title":"Controllable Text-to-Image Generation","date":"2019-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taki0112/ControlGAN-Tensorflow","path":"utils.py","file_url":"https://github.com/taki0112/ControlGAN-Tensorflow/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a991a23f7dd903f5","mcp_get_code":{"code_sha256":"a991a23f7dd903f5"}},{"arxiv_id":"1909.03935","paper":"/paper/gan-leaks-a-taxonomy-of-membership-inference","title":"GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models","date":"2019-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DingfanChen/GAN-Leaks","path":"gan_models/pggan/misc.py","file_url":"https://github.com/DingfanChen/GAN-Leaks/blob/HEAD/gan_models/pggan/misc.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90a97f8af1b8f269","mcp_get_code":{"code_sha256":"90a97f8af1b8f269"}},{"arxiv_id":"1903.06048","paper":"/paper/msg-gan-multi-scale-gradients-gan-for-more","title":"MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks","date":"2019-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akanimax/BMSG-GAN","path":"sourcecode/generate_multi_scale_samples.py","file_url":"https://github.com/akanimax/BMSG-GAN/blob/HEAD/sourcecode/generate_multi_scale_samples.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bd6383ad21a9d1b9","mcp_get_code":{"code_sha256":"bd6383ad21a9d1b9"}},{"arxiv_id":"1903.06048","paper":"/paper/msg-gan-multi-scale-gradients-gan-for-more","title":"MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks","date":"2019-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akanimax/BMSG-GAN","path":"sourcecode/latent_space_interpolation.py","file_url":"https://github.com/akanimax/BMSG-GAN/blob/HEAD/sourcecode/latent_space_interpolation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbf23ae0401556fa","mcp_get_code":{"code_sha256":"bbf23ae0401556fa"}},{"arxiv_id":"1901.10277","paper":"/paper/self-supervised-deep-image-denoising","title":"High-Quality Self-Supervised Deep Image Denoising","date":"2019-01-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/selfsupervised-denoising","path":"selfsupervised_denoising.py","file_url":"https://github.com/NVlabs/selfsupervised-denoising/blob/HEAD/selfsupervised_denoising.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"90a97f8af1b8f269","mcp_get_code":{"code_sha256":"90a97f8af1b8f269"}},{"arxiv_id":"1812.04948","paper":"/paper/a-style-based-generator-architecture-for","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","date":"2018-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taki0112/StyleGAN-Tensorflow","path":"utils.py","file_url":"https://github.com/taki0112/StyleGAN-Tensorflow/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a991a23f7dd903f5","mcp_get_code":{"code_sha256":"a991a23f7dd903f5"}},{"arxiv_id":"1710.10196","paper":"/paper/progressive-growing-of-gans-for-improved","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","date":"2017-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NikhitaMethwani/Deep-Fakes-Cars-Generation","path":"misc.py","file_url":"https://github.com/NikhitaMethwani/Deep-Fakes-Cars-Generation/blob/HEAD/misc.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90a97f8af1b8f269","mcp_get_code":{"code_sha256":"90a97f8af1b8f269"}},{"arxiv_id":"1710.10196","paper":"/paper/progressive-growing-of-gans-for-improved","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","date":"2017-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexeyhorkin/ProGAN-PyTorch","path":"samples/generate_samples.py","file_url":"https://github.com/alexeyhorkin/ProGAN-PyTorch/blob/HEAD/samples/generate_samples.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bd6383ad21a9d1b9","mcp_get_code":{"code_sha256":"bd6383ad21a9d1b9"}},{"arxiv_id":"1710.10196","paper":"/paper/progressive-growing-of-gans-for-improved","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","date":"2017-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexeyhorkin/ProGAN-PyTorch","path":"samples/latent_space_interpolation.py","file_url":"https://github.com/alexeyhorkin/ProGAN-PyTorch/blob/HEAD/samples/latent_space_interpolation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbf23ae0401556fa","mcp_get_code":{"code_sha256":"bbf23ae0401556fa"}},{"arxiv_id":"1710.10196","paper":"/paper/progressive-growing-of-gans-for-improved","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","date":"2017-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akanimax/pro_gan_pytorch","path":"pro_gan_pytorch/utils.py","file_url":"https://github.com/akanimax/pro_gan_pytorch/blob/HEAD/pro_gan_pytorch/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f6a1e7dfa6b60993","mcp_get_code":{"code_sha256":"f6a1e7dfa6b60993"}}]}