Pytorch implementation for stereo matching described in the paper: Efficient Deep learning for stereo matching
-
Updated
Jan 20, 2019 - Python
Pytorch implementation for stereo matching described in the paper: Efficient Deep learning for stereo matching
[NeurIPS 2019 Google MicroNet Challenge] MSUNet is an efficient model that won the 4th place in the Google MicroNet Challenge CIFAR-100 Track hosted at NeurIPS 2019 designed by Yu Zheng, Shen Yan, Mi Zhang
[ECCV 2020 Oral] MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution
Efficient Deep Learning for Stereo Matching Tensorflow 2.x
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)
[ICLR 2022] "Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently", by Xiaohan Chen, Jason Zhang and Zhangyang Wang.
Code repository of the paper "Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups" https://proceedings.mlr.press/v162/knigge22a.html
A generic code base for neural network pruning, especially for pruning at initialization.
This repository provides implementation of a baseline method and our proposed methods for efficient Skeleton-based Human Action Recognition.
Recent Advances on Efficient Vision Transformers
[NeurIPS2022] Official implementation of the paper 'Green Hierarchical Vision Transformer for Masked Image Modeling'.
Code and resources on scalable and efficient Graph Neural Networks
[Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning
NeurIPS 2021, Official codes for "Efficient Training of Visual Transformers with Small Datasets".
Efficient Deep Learning for Real-time Classification of Astronomical Transients and Multivariate Time-series
Frame Flexible Network (CVPR2023)
[ICLR'23] Trainability Preserving Neural Pruning (PyTorch)
NeurIPS 2023
This repository is for reproducing the results shown in the NNCodec ICML Workshop paper. Additionally, it includes a demo, prepared for the Neural Compression Workshop (NCW).
[ICLR 2022] Data-Efficient Graph Grammar Learning for Molecular Generation
Add a description, image, and links to the efficient-deep-learning topic page so that developers can more easily learn about it.
To associate your repository with the efficient-deep-learning topic, visit your repo's landing page and select "manage topics."