{"id":17242093,"url":"https://github.com/byte7/deep-learning-papers-implementation","last_synced_at":"2025-10-16T08:00:22.625Z","repository":{"id":79057380,"uuid":"161365622","full_name":"Byte7/Deep-Learning-Papers-Implementation","owner":"Byte7","description":"Implementing popular DL papers","archived":false,"fork":false,"pushed_at":"2018-12-25T05:37:37.000Z","size":15386,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-31T01:34:45.307Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep-Learning-Papers-Implementation\nImplementing popular DL papers\n\n\n#### Architectures\n\n- [X] AlexNet [`paper`](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)\n- [X] ZFNet [`paper`](https://arxiv.org/abs/1311.2901)\n- [ ] VGG16 [`paper1`](https://arxiv.org/abs/1505.06798) [`paper2`](https://arxiv.org/pdf/1409.1556.pdf)\n- [ ] ResNet [`paper`](https://arxiv.org/abs/1704.06904)\n- [ ] GoogLeNet [`paper`](https://arxiv.org/abs/1409.4842)\n- [ ] Inception [`paper`](https://arxiv.org/abs/1512.00567)\n- [ ] Xception [`paper`](https://arxiv.org/abs/1610.02357)\n- [ ] MobileNet [`paper`](https://arxiv.org/abs/1704.04861)\n\n#### Semantic Segmentation\n\n- [ ] FCN [`paper`](https://arxiv.org/abs/1411.4038)\n- [ ] SegNet [`paper`](https://arxiv.org/abs/1511.00561)\n- [ ] UNet [`paper`](https://arxiv.org/abs/1505.04597)\n- [ ] PSPNet [`paper`](https://arxiv.org/abs/1612.01105)\n- [ ] DeepLab [`paper`](https://arxiv.org/abs/1606.00915)\n- [ ] ICNet [`paper`](https://arxiv.org/abs/1704.08545)\n- [ ] ENet [`paper`](https://arxiv.org/abs/1606.02147)\n\n#### Generative adversarial networks\n\n- [X] GAN [`paper`](https://arxiv.org/abs/1406.2661)\n- [X] DCGAN [`paper`](https://arxiv.org/abs/1511.06434)\n- [ ] WGAN [`paper`](https://arxiv.org/abs/1701.07875)\n- [ ] Pix2Pix [`paper`](https://arxiv.org/abs/1611.07004)\n- [ ] CycleGAN [`paper`](https://arxiv.org/abs/1703.10593)\n- [ ] Progressive GAN [`paper`](#)\n\n#### Object detection\n\n- [ ] RCNN [`paper`](https://arxiv.org/abs/1311.2524)\n- [ ] Fast-RCNN [`paper`](https://arxiv.org/abs/1504.08083)\n- [ ] Faster-RCNN [`paper`](https://arxiv.org/abs/1506.01497)\n- [ ] SSD [`paper`](https://arxiv.org/abs/1512.02325)\n- [ ] YOLO [`paper`](https://arxiv.org/abs/1506.02640)\n- [ ] YOLO9000 [`paper`](https://arxiv.org/abs/1612.08242)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbyte7%2Fdeep-learning-papers-implementation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbyte7%2Fdeep-learning-papers-implementation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbyte7%2Fdeep-learning-papers-implementation/lists"}