Several TensorFlow implementations of recent papers based on the tensorpack framework.
Unfortunately, there is a difference between re-implementing deep-learning papers, and re-obtaining the published performance. The latter usually requires tedious hyper-parameter optimization amongst other things like very long training times. Hence, the following implementations have no guarantees to get the published performance. However you can judge this yourself using our pretrained models.
PWC (Sun et al., CVPR 2018) [pdf] [model PWC] PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Learning To See in the Dark (Chen et al., CVPR 2018) [pdf] [pretrained model] Learning to See in the Dark
ProgressiveGrowingGan (Karras et al., ICLR 2018) [pdf] Progressive Growing of GANs for Improved Quality, Stability, and Variation
EnhanceNet (Sajjadi et al., ICCV 2017) [pdf] [pretrained model] EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
FlowNet2 (Ilg et al., CVPR 2017) [pdf] [model FlowNet2-S] [model FlowNet2-C] [model FlowNet2] FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
LetThereBeColor (Iizuka et al., SIGGRAPH 2016) [pdf] [pretrained model] Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification
DeepVideoDeblurring (Su et al., CVPR 2017) [pdf] Deep Video Deblurring
SplitBrainAutoEncoder (Zhang et al., CVPR 2017) [pdf] Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
PointNet (Qi et al., CVPR 2017) [pdf] [pretrained model] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
SubPixelSuperResolution (Shi et al., CVPR 216) [pdf] Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
ImageRestorationSymmetricSkip (Mao et al., NIPS 2016 [pdf] Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
AlphaGo (Silver et al, Nature 2016) [pdf]
DynamicFilterNetwork (Brabandere et al., NIPS 2016) [pdf] Dynamic Filter Network
I do not judge the papers and methods. Reproducing deep-learning papers with meaningful performance is difficult. So there can be some tricks, I missed. There is no motivation/time to make them all work perfectly -- when possible.
model
means a pre-trained model provided by the authors and ported to TensorFlow
pre-trained model
means a model training with the provided script above