A list of all papers on unsupervised and weakly supervised learning.
awesome-unsupervised--learning
A list of all papers on unsupervised and weakly supervised learning.
- Context Encoder Link: http://graphics.cs.cmu.edu/projects/deepContext/
- Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles Link: https://arxiv.org/pdf/1603.09246.pdf
- Unsupervised Learning of Visual Representations using Videos Link:http://www.cs.cmu.edu/~xiaolonw/unsupervise.html
- Shuffle and Learn: Unsupervised Learning using Temporal Order Verification Link: https://arxiv.org/pdf/1603.08561.pdf
- Context Encoders: Feature Learning by Inpainting Link: http://people.eecs.berkeley.edu/~pathak/context_encoder/
- Generative Face Completion Link: http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Generative_Face_Completion_CVPR_2017_paper.pdf
- Learning Motion Patterns in Videos Link: https://arxiv.org/pdf/1612.07217.pdf
- Learning Features by Watching Objects Move Link: https://arxiv.org/pdf/1612.06370.pdf
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Link: https://arxiv.org/pdf/1511.06434.pdf
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Link: https://junyanz.github.io/CycleGAN/
- Globally and Locally Consistent Image Completion Link: http://hi.cs.waseda.ac.jp/~iizuka/projects/completion/en/
- High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis Link: https://arxiv.org/pdf/1611.09969.pdf
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Link: https://arxiv.org/pdf/1609.04802.pdf
- Wasserstein GAN Link: https://arxiv.org/pdf/1701.07875.pdf
- Unsupervised Learning of Video Representations using LSTMs Link: https://arxiv.org/pdf/1502.04681.pdf
- Spatial contrasting for deep unsupervised learning Link: https://arxiv.org/pdf/1610.00243.pdf
- Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning Link: https://arxiv.org/pdf/1605.08104.pdf
- MoCoGAN: Decomposing Motion and Content for Video Generation Link: https://arxiv.org/pdf/1707.04993.pdf