sbrugman/deep-learning-papers

Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled. RenderGAN: Generating Realistic Labeled Data, nov 2016, arxiv Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, feb 2016, arxiv SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5MB model size, feb 2016, arxiv Snapshot Ensembles: Train 1, Get M for Free, 2016, paper, github