feat: license, new projects and contribution guidelines

LICENSE.txt

@@ -0,0 +1,7 @@
+Copyright (c) 2017 Ritchie Ng
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -44,11 +44,30 @@ This is inspired by the famous [Awesome TensorFlow](https://github.com/jtoy/awes
 	- ResNet model in PyTorch.
 - [Wide Residual Networks](https://github.com/szagoruyko/functional-zoo)
 	- Wide ResNet model in PyTorch
+- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://github.com/longcw/faster_rcnn_pytorch)
 
 ## Projects in Pytorch
 - [Reinforcement learning models in ViZDoom environment with PyTorch](https://github.com/akolishchak/doom-net-pytorch)
+- [Collection of Generative Models with PyTorch](https://github.com/wiseodd/generative-models)
+	- Generative Adversarial Nets (GAN)
+		1. [Vanilla GAN](https://arxiv.org/abs/1406.2661)
+		2. [Conditional GAN](https://arxiv.org/abs/1411.1784)
+		3. [InfoGAN](https://arxiv.org/abs/1606.03657)
+		4. [Wasserstein GAN](https://arxiv.org/abs/1701.07875)
+		5. [Mode Regularized GAN](https://arxiv.org/abs/1612.02136)
+	- Variational Autoencoder (VAE)
+		1. [Vanilla VAE](https://arxiv.org/abs/1312.6114)
+		2. [Conditional VAE](https://arxiv.org/abs/1406.5298)
+		3. [Denoising VAE](https://arxiv.org/abs/1511.06406)
+		4. [Adversarial Autoencoder](https://arxiv.org/abs/1511.05644)
+		5. [Adversarial Variational Bayes](https://arxiv.org/abs/1701.04722)
 
 ## Community
 - [PyTorch Discussion Forum](https://discuss.pytorch.org/)
 	- This is actively maintained by [Adam Paszke](https://github.com/apaszke)
-- [StackOverFlow PyTorch Tags](http://stackoverflow.com/questions/tagged/pytorch)
+- [StackOverFlow PyTorch Tags](http://stackoverflow.com/questions/tagged/pytorch)
+
+## Contributions
+Do feel free to contribute! 
+
+You can raise an isssue or submit a pull request, whichever is more convenient for you. The guideline is simple: just follow the format of the previous bullet point.