A state-of-the-art semi-supervised method for image recognition
-
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"
A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2017
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"
Hiding Images within other images using Deep Learning
Tensorflow implementation of NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
Convolution dictionary learning for time-series
Implementation for <Deep Hyperspherical Learning> in NIPS'17.
Code for "Effective Dimensionality Reduction for Word Embeddings".
Binary Convolution Network for faster real-time processing in ASICs
Implementation of the paper : "Toward Multimodal Image-to-Image Translation"
End-to-End Differentiable Proving
Global NIPS Paper Implementation Challenge of "Hiding Images in Plain Sight: Deep Steganography"
Our NIPS 2017: Learning to Run source code
text convolution-deconvolution auto-encoder model in PyTorch
Structured Bayesian Pruning, NIPS 2017
Reason8.ai PyTorch solution for NIPS RL 2017 challenge
Add a description, image, and links to the nips-2017 topic page so that developers can more easily learn about it.
To associate your repository with the nips-2017 topic, visit your repo's landing page and select "manage topics."