A state-of-the-art semi-supervised method for image recognition
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Updated
Oct 8, 2020 - Python
A state-of-the-art semi-supervised method for image recognition
Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
Hardnet descriptor model - "Working hard to know your neighbor's margins: Local descriptor learning loss"
Code/Model release for NIPS 2017 paper "Attentional Pooling for Action Recognition"
Code for "Effective Dimensionality Reduction for Word Embeddings".
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
Implementation for <Deep Hyperspherical Learning> in NIPS'17.
Convolution dictionary learning for time-series
Reason8.ai PyTorch solution for NIPS RL 2017 challenge
PyTorch re-implementation of parts of "Deep Sets" (NIPS 2017)
Implementation of the paper : "Toward Multimodal Image-to-Image Translation"
text convolution-deconvolution auto-encoder model in PyTorch
Our NIPS 2017: Learning to Run source code
Code for NIPS 2017 learning to run challenge
Chainer implementation of the paper "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results" (https://arxiv.org/abs/1703.01780)
Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)
Reproduction code for WGAN-LP
Fast-Slow Recurrent Neural Networks
Tensorflow Implementation of adversarial learning based adversarial example generator
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