{"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/lrelu","entry":"lrelu","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":33,"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":27,"n_samples_ran":3,"n_samples_fingerprinted":3,"n_places":34,"n_places_pointer_only":4,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":24},"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":"2401.00825","paper":"/paper/sharp-nerf-grid-based-fast-deblurring-neural","title":"Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Fields Using Sharpness Prior","date":"2024-01-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"benhenryl/sharpnerf","path":"preprocess_focus.py","file_url":"https://github.com/benhenryl/sharpnerf/blob/HEAD/preprocess_focus.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9251b1037c26cc83","mcp_get_code":{"code_sha256":"9251b1037c26cc83"}},{"arxiv_id":"2303.08658","paper":"/paper/skinned-motion-retargeting-with-residual","title":"Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry","date":"2023-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kebii/R2ET","path":"src/ops.py","file_url":"https://github.com/Kebii/R2ET/blob/HEAD/src/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9acfdca54949c838","mcp_get_code":{"code_sha256":"9acfdca54949c838"}},{"arxiv_id":"2212.13824","paper":"/paper/multi-realism-image-compression-with-a","title":"Multi-Realism Image Compression with a Conditional Generator","date":"2022-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Nikolai10/MRIC","path":"src/compare_gan_tf2/arch_ops.py","file_url":"https://github.com/Nikolai10/MRIC/blob/HEAD/src/compare_gan_tf2/arch_ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c13acb298b68dca5","mcp_get_code":{"code_sha256":"c13acb298b68dca5"}},{"arxiv_id":"2208.01587","paper":"/paper/learning-to-incorporate-texture-saliency","title":"Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization","date":"2022-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XiangGao1102/CTSS","path":"net/generator.py","file_url":"https://github.com/XiangGao1102/CTSS/blob/HEAD/net/generator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d31084f599747086","mcp_get_code":{"code_sha256":"d31084f599747086"}},{"arxiv_id":"2110.05770","paper":"/paper/hypercube-implicit-field-representations-of-1","title":"HyperCube: Implicit Field Representations of Voxelized 3D Models","date":"2021-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mproszewska/hypercube","path":"latent_GAN/ops.py","file_url":"https://github.com/mproszewska/hypercube/blob/HEAD/latent_GAN/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"017a6fa9ecaa8817","mcp_get_code":{"code_sha256":"017a6fa9ecaa8817"}},{"arxiv_id":"2105.02400","paper":"/paper/sipsa-net-shift-invariant-pan-sharpening-with","title":"SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery","date":"2021-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brachiohyup/SIPSA","path":"net.py","file_url":"https://github.com/brachiohyup/SIPSA/blob/HEAD/net.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"22176e23053c3704","mcp_get_code":{"code_sha256":"22176e23053c3704"}},{"arxiv_id":"2010.13219","paper":"/paper/ir-gan-room-impulse-response-generator-for","title":"IR-GAN: Room Impulse Response Generator for Far-field Speech Recognition","date":null,"month_inferred_from_arxiv_id":"2020-10","title_source":"archive","repo":"GAMMA-UMD/IR-GAN","path":"wavegan.py","file_url":"https://github.com/GAMMA-UMD/IR-GAN/blob/HEAD/wavegan.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f9251347b36fbe6","mcp_get_code":{"code_sha256":"6f9251347b36fbe6"}},{"arxiv_id":"2007.03059","paper":"/paper/guided-fine-tuning-for-large-scale-material","title":"Guided Fine-Tuning for Large-Scale Material Transfer","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valentin-deschaintre/Guided_fine_tuning_SVBRDF","path":"tfHelpers.py","file_url":"https://github.com/valentin-deschaintre/Guided_fine_tuning_SVBRDF/blob/HEAD/tfHelpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"19af0fe9ccd63f6b","mcp_get_code":{"code_sha256":"19af0fe9ccd63f6b"}},{"arxiv_id":"2004.09805","paper":"/paper/amc-loss-angular-margin-contrastive-loss-for","title":"AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification","date":"2020-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hchoi71/AMC-Loss","path":"utils.py","file_url":"https://github.com/hchoi71/AMC-Loss/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":"d1663043431bd74c","mcp_get_code":{"code_sha256":"d1663043431bd74c"}},{"arxiv_id":"2003.08723","paper":"/paper/latent-space-subdivision-stable-and","title":"Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow","date":"2020-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lij131/LatentSpaceSubdivision-base","path":"ops.py","file_url":"https://github.com/lij131/LatentSpaceSubdivision-base/blob/HEAD/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe116d2c6ecc52cf","mcp_get_code":{"code_sha256":"fe116d2c6ecc52cf"}},{"arxiv_id":"2001.02905","paper":"/paper/fast-adaptation-to-super-resolution-networks","title":"Fast Adaptation to Super-Resolution Networks via Meta-Learning","date":"2020-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"parkseobin/MLSR","path":"IDN_definition.py","file_url":"https://github.com/parkseobin/MLSR/blob/HEAD/IDN_definition.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6392a605be0ec82c","mcp_get_code":{"code_sha256":"6392a605be0ec82c"}},{"arxiv_id":"1908.01311","paper":"/paper/fully-automatic-video-colorization-with-self-1","title":"Fully Automatic Video Colorization with Self-Regularization and Diversity","date":"2019-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChenyangLEI/Fully-Automatic-Video-Colorization-with-Self-Regularization-and-Diversity","path":"network.py","file_url":"https://github.com/ChenyangLEI/Fully-Automatic-Video-Colorization-with-Self-Regularization-and-Diversity/blob/HEAD/network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f406422099514e4c","mcp_get_code":{"code_sha256":"f406422099514e4c"}},{"arxiv_id":"1906.11557","paper":"/paper/flexible-svbrdf-capture-with-a-multi-image","title":"Flexible SVBRDF Capture with a Multi-Image Deep Network","date":null,"month_inferred_from_arxiv_id":"2019-06","title_source":"archive","repo":"valentin-deschaintre/multi-image-deepNet-SVBRDF-acquisition","path":"tfHelpers.py","file_url":"https://github.com/valentin-deschaintre/multi-image-deepNet-SVBRDF-acquisition/blob/HEAD/tfHelpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"19af0fe9ccd63f6b","mcp_get_code":{"code_sha256":"19af0fe9ccd63f6b"}},{"arxiv_id":"1905.10994","paper":"/paper/ode2vae-deep-generative-second-order-odes","title":"ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks","date":"2019-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cagatayyildiz/ODE2VAE","path":"model/tf_utils.py","file_url":"https://github.com/cagatayyildiz/ODE2VAE/blob/HEAD/model/tf_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c37722f2da367b94","mcp_get_code":{"code_sha256":"c37722f2da367b94"}},{"arxiv_id":"1905.08171","paper":"/paper/semi-supervised-learning-by-augmented","title":"Semi-Supervised Learning by Augmented Distribution Alignment","date":"2019-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qinenergy/adanet","path":"convlarge/layers.py","file_url":"https://github.com/qinenergy/adanet/blob/HEAD/convlarge/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"27b7ebb68a597e6f","mcp_get_code":{"code_sha256":"27b7ebb68a597e6f"}},{"arxiv_id":"1904.09117","paper":"/paper/selflow-self-supervised-learning-of-optical","title":"SelFlow: Self-Supervised Learning of Optical Flow","date":"2019-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ppliuboy/SelFlow","path":"utils.py","file_url":"https://github.com/ppliuboy/SelFlow/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":"69e0961ea54f416c","mcp_get_code":{"code_sha256":"69e0961ea54f416c"}},{"arxiv_id":"1902.09145","paper":"/paper/ddflow-learning-optical-flow-with-unlabeled","title":"DDFlow: Learning Optical Flow with Unlabeled Data Distillation","date":"2019-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ppliuboy/DDFlow","path":"utils.py","file_url":"https://github.com/ppliuboy/DDFlow/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":"69e0961ea54f416c","mcp_get_code":{"code_sha256":"69e0961ea54f416c"}},{"arxiv_id":"1811.11212","paper":"/paper/self-supervised-generative-adversarial","title":"Self-Supervised GANs via Auxiliary Rotation Loss","date":"2018-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangqianhui/Self-Supervised-GANs","path":"ops.py","file_url":"https://github.com/zhangqianhui/Self-Supervised-GANs/blob/HEAD/ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"207527695edefd17","mcp_get_code":{"code_sha256":"207527695edefd17"}},{"arxiv_id":"1809.10636","paper":"/paper/conditional-wavegan","title":"Conditional WaveGAN","date":"2018-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"acheketa/cwavegan","path":"tpu/tpu_model.py","file_url":"https://github.com/acheketa/cwavegan/blob/HEAD/tpu/tpu_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f9251347b36fbe6","mcp_get_code":{"code_sha256":"6f9251347b36fbe6"}},{"arxiv_id":"1806.10522","paper":"/paper/speech-denoising-with-deep-feature-losses","title":"Speech Denoising with Deep Feature Losses","date":"2018-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"francoisgermain/SpeechDenoisingWithDeepFeatureLosses","path":"helper.py","file_url":"https://github.com/francoisgermain/SpeechDenoisingWithDeepFeatureLosses/blob/HEAD/helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a2446e08ebe5a2b","mcp_get_code":{"code_sha256":"1a2446e08ebe5a2b"}},{"arxiv_id":"1806.05376","paper":"/paper/single-image-reflection-separation-with","title":"Single Image Reflection Separation with Perceptual Losses","date":"2018-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ceciliavision/perceptual-reflection-removal","path":"discriminator.py","file_url":"https://github.com/ceciliavision/perceptual-reflection-removal/blob/HEAD/discriminator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"89c23bcf39c231d9","mcp_get_code":{"code_sha256":"89c23bcf39c231d9"}},{"arxiv_id":"1806.02311","paper":"/paper/unsupervised-attention-guided-image-to-image","title":"Unsupervised Attention-guided Image to Image Translation","date":"2018-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation","path":"layers.py","file_url":"https://github.com/AlamiMejjati/Unsupervised-Attention-guided-Image-to-Image-Translation/blob/HEAD/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"59154719255f5869","mcp_get_code":{"code_sha256":"59154719255f5869"}},{"arxiv_id":"1805.01934","paper":"/paper/learning-to-see-in-the-dark","title":"Learning to See in the Dark","date":"2018-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cchen156/Learning-to-See-in-the-Dark","path":"train_Fuji.py","file_url":"https://github.com/cchen156/Learning-to-See-in-the-Dark/blob/HEAD/train_Fuji.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f406422099514e4c","mcp_get_code":{"code_sha256":"f406422099514e4c"}},{"arxiv_id":"1804.09337","paper":"/paper/learning-a-discriminative-feature-network-for","title":"Learning a Discriminative Feature Network for Semantic Segmentation","date":"2018-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuhuiMa/DFN-tensorflow","path":"components.py","file_url":"https://github.com/YuhuiMa/DFN-tensorflow/blob/HEAD/components.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"19af0fe9ccd63f6b","mcp_get_code":{"code_sha256":"19af0fe9ccd63f6b"}},{"arxiv_id":"1711.01558","paper":"/paper/wasserstein-auto-encoders","title":"Wasserstein Auto-Encoders","date":"2017-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tolstikhin/wae","path":"ops.py","file_url":"https://github.com/tolstikhin/wae/blob/HEAD/ops.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":"fde9f573ace8d77f","mcp_get_code":{"code_sha256":"fde9f573ace8d77f"}},{"arxiv_id":"1702.08658","paper":"/paper/towards-deeper-understanding-of-variational","title":"Towards Deeper Understanding of Variational Autoencoding Models","date":"2017-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShengjiaZhao/Generalized-PixelVAE","path":"pixel_cnn_pp/encoder.py","file_url":"https://github.com/ShengjiaZhao/Generalized-PixelVAE/blob/HEAD/pixel_cnn_pp/encoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4661a1e5a82037d4","mcp_get_code":{"code_sha256":"4661a1e5a82037d4"}},{"arxiv_id":"1701.06487","paper":"/paper/dirty-pixels-optimizing-image-classification","title":"Dirty Pixels: Towards End-to-End Image Processing and Perception","date":"2017-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-computational-imaging/DirtyPixels","path":"nets/unet.py","file_url":"https://github.com/princeton-computational-imaging/DirtyPixels/blob/HEAD/nets/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f406422099514e4c","mcp_get_code":{"code_sha256":"f406422099514e4c"}},{"arxiv_id":"1612.02297","paper":"/paper/spatially-adaptive-computation-time-for","title":"Spatially Adaptive Computation Time for Residual Networks","date":"2016-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mfigurnov/sact","path":"cifar_model.py","file_url":"https://github.com/mfigurnov/sact/blob/HEAD/cifar_model.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":"4b998dd1f53d28b4","mcp_get_code":{"code_sha256":"4b998dd1f53d28b4"}},{"arxiv_id":"1611.07004","paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awjuliani/Pix2Pix-Film","path":"helper.py","file_url":"https://github.com/awjuliani/Pix2Pix-Film/blob/HEAD/helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3270c9ec35a576f9","mcp_get_code":{"code_sha256":"3270c9ec35a576f9"}},{"arxiv_id":"1603.08155","paper":"/paper/perceptual-losses-for-real-time-style","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","date":"2016-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thatbrguy/Dehaze-GAN","path":"legacy/utils.py","file_url":"https://github.com/thatbrguy/Dehaze-GAN/blob/HEAD/legacy/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f1021e0f87be533e","mcp_get_code":{"code_sha256":"f1021e0f87be533e"}},{"arxiv_id":"1603.04042","paper":"/paper/deep-interactive-object-selection","title":"Deep Interactive Object Selection","date":"2016-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IntelVCL/Intseg","path":"IntSeg_Train.py","file_url":"https://github.com/IntelVCL/Intseg/blob/HEAD/IntSeg_Train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f406422099514e4c","mcp_get_code":{"code_sha256":"f406422099514e4c"}},{"arxiv_id":"1511.06434","paper":"/paper/unsupervised-representation-learning-with-1","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","date":"2015-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NicicDamjan/SoftKompjuting","path":"DCGAN.py","file_url":"https://github.com/NicicDamjan/SoftKompjuting/blob/HEAD/DCGAN.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0273e02ae86d0147","mcp_get_code":{"code_sha256":"0273e02ae86d0147"}},{"arxiv_id":"1511.06434","paper":"/paper/unsupervised-representation-learning-with-1","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","date":"2015-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elybecker/dcgan","path":"model.py","file_url":"https://github.com/elybecker/dcgan/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91ec4d061d94978f","mcp_get_code":{"code_sha256":"91ec4d061d94978f"}},{"arxiv_id":"Zhang_Dispel_Darkness_for_Better_Fusion_A_Controllable_Visual_Enhancer_based_CVPR_2024_paper","paper":null,"title":"arXiv:Zhang_Dispel_Darkness_for_Better_Fusion_A_Controllable_Visual_Enhancer_based_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HaoZhang1018/DDBF","path":"model.py","file_url":"https://github.com/HaoZhang1018/DDBF/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5354eb4115b864bc","mcp_get_code":{"code_sha256":"5354eb4115b864bc"}}]}