{"id":16739486,"url":"https://github.com/williamfalcon/keras-deep-learning-paper-implementations","last_synced_at":"2025-10-26T10:09:41.542Z","repository":{"id":75973657,"uuid":"86275168","full_name":"williamFalcon/keras-deep-learning-paper-implementations","owner":"williamFalcon","description":"Keras implementation of Deep Learning papers","archived":false,"fork":false,"pushed_at":"2018-07-25T21:41:28.000Z","size":39,"stargazers_count":95,"open_issues_count":1,"forks_count":32,"subscribers_count":12,"default_branch":"master","last_synced_at":"2025-01-22T11:47:42.477Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/williamFalcon.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-03-27T00:40:26.000Z","updated_at":"2025-01-03T06:43:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"462744bd-962c-4b34-93b2-ce5702bfec22","html_url":"https://github.com/williamFalcon/keras-deep-learning-paper-implementations","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/williamFalcon%2Fkeras-deep-learning-paper-implementations","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/williamFalcon%2Fkeras-deep-learning-paper-implementations/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/williamFalcon%2Fkeras-deep-learning-paper-implementations/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/williamFalcon%2Fkeras-deep-learning-paper-implementations/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/williamFalcon","download_url":"https://codeload.github.com/williamFalcon/keras-deep-learning-paper-implementations/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243801611,"owners_count":20350106,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-13T00:51:31.082Z","updated_at":"2025-10-26T10:09:41.445Z","avatar_url":"https://github.com/williamFalcon.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Keras Deep Learning Paper Implementations    \n\nA curated list of implementations in keras.      \n\nIt's a bit of a hassle to find implementation of most of the latest papers. Hopefully this allows anyone to get up and running with the state-of-the-art networks in little to no time.    \n    \n**We welcome your contributions!**\n\nIf you have any paper/code suggestions, please feel free to edit and sumbit a pull request.\n\n---   \n\n### Imagenet Models    \n- Alexnet (2012), A. Krizhevsky et al. [[pdf]](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) [[code]](https://gist.github.com/JBed/c2fb3ce8ed299f197eff)    \n- VGG16 (2014), K. Simonyan et al. [[pdf]](https://arxiv.org/pdf/1409.1556.pdf) [[code]](https://github.com/fchollet/deep-learning-models/blob/master/vgg16.py)\n- VGG19 (2014), K. Simonyan et al. [[pdf]](https://arxiv.org/pdf/1409.1556.pdf)  [[code]](https://github.com/fchollet/deep-learning-models/blob/master/vgg19.py)\n- Resnet (2015), K. He et al. [[pdf]](https://arxiv.org/pdf/1512.03385.pdf)  [[code]](https://github.com/raghakot/keras-resnet)    \n\n\n### Unsupervised / Generative Models    \n\n- Pix2Pix. Image-to-Image Translation with Conditional Adversarial Networks (2016), P. Isola et al. [[pdf]](https://arxiv.org/pdf/1611.07004.pdf) [[code]](https://github.com/williamFalcon/pix2pix-keras)\n- Deepmind's wavenet (2016), Van den Oord et al. [[pdf]](https://arxiv.org/pdf/1609.03499.pdf) [[code]](https://github.com/usernaamee/keras-wavenet)\n-  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. (2016), C. Ledig et al. [[pdf]](https://arxiv.org/pdf/1609.04802v2.pdf) [[code]](https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks)\n\n### Convolutional Models  \n- XCeption (2016), F. Chollet. [[pdf]](https://arxiv.org/pdf/1610.02357.pdf) [[code]](https://github.com/fchollet/deep-learning-models/blob/master/xception.py)\n- Inception v3 (2015), C. Szegedy et al. [[pdf]](https://arxiv.org/pdf/1512.00567.pdf) [[code]](https://github.com/fchollet/deep-learning-models/blob/master/inception_v3.py)    \n-  Image Super-Resolution Using Deep Convolutional Networks (2015), C. Dong et al. [[pdf]](https://arxiv.org/pdf/1501.00092v3.pdf) [[code]](https://github.com/titu1994/Image-Super-Resolution)\n- Time-series modeling with undecimated fully convolutional neural networks (2015), R. Mittelman. [[pdf]](https://arxiv.org/pdf/1508.00317.pdf) [[code]](https://github.com/lukovkin/ufcnn-keras)\n- DenseNet: Densely Connected Convolutional Network (2016), G. Huang. [[pdf]](https://arxiv.org/abs/1608.06993) [[code]](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet)\n\n### LSTM    \n- Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (2016), D. Neil [[pdf]](https://arxiv.org/pdf/1610.09513.pdf) [[code]](https://github.com/fferroni/PhasedLSTM-Keras)    \n- Bidirectional LSTM: Neural Architectures for Named Entity Recognition (2016), G. Lample [[pdf]](http://www.aclweb.org/anthology/N16-1030) [[code]](https://gist.github.com/dirko/1d596ca757a541da96ac3caa6f291229)    \n\n### Fun Models  \n\n-  Deep Dream. Inceptionism: Going Deeper into Neural Networks (2015), A. Mordvintsev et al. at Google. [[blog]](https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html) [[code]](https://github.com/fchollet/keras/blob/master/examples/deep_dream.py)\n-  Style Transfer. Image Style Transfer Using Convolutional Neural Networks (2016), L. Gatys. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) [[code]](https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py)\n-  Fast (realtime) Neural Style Transfer. Perceptual Losses for Real-Time Style Transfer and Super-Resolution (2016), J. Johnson. [[pdf]](https://arxiv.org/pdf/1603.08155.pdf) [[code]](https://github.com/titu1994/Fast-Neural-Style)    \n\n## Acknowledgement\n\nThank you for all your contributions.\n\n## License\nMIT    \n\nTo the extent possible under law, [William Falcon](https://williamfalcon.com) has waived all copyright and related or neighboring rights to this work.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwilliamfalcon%2Fkeras-deep-learning-paper-implementations","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwilliamfalcon%2Fkeras-deep-learning-paper-implementations","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwilliamfalcon%2Fkeras-deep-learning-paper-implementations/lists"}