Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks

Last update: May 19, 2022

Related tags

Deep Learning hidden-fold-networks

Overview

Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks

by Ángel López García-Arias, Masanori Hashimoto, Masato Motomura, and Jaehoon Yu

Accepted at The British Machine Vision Conference (BMVC) 2021.

arXiv link

The code in this repository was originally developed from a fork of hidden-networks.

Requirements

Python 3.6 or higher. CUDA 11.0.
For library requirements see requirements.txt.

Usage

Experiment configurations are defined in yaml files under configs/.
These configurations can be overridden by setting parameters directly through arguments (see args.py).

Example 1: HFN-ResNet50, top-k%=30%, CIFAR100

python3 main.py --config configs/CIFAR100/HFN_ResNet50_3_4.yaml --multigpu 0

Example 2: HFN-ResNet50, top-k%=50%, CIFAR100, 100 epochs

python3 main.py --config configs/CIFAR100/HFN_ResNet50_3_4.yaml --multigpu 0 --top_k=0.5 --epochs 100

Owner

Ángel López García-Arias

AI Computing Research Unit, Tokyo Institute of Technology

GitHub Repository

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Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks

Related tags

Overview

Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks

Requirements

Usage

Example 1: HFN-ResNet50, top-k%=30%, CIFAR100

Example 2: HFN-ResNet50, top-k%=50%, CIFAR100, 100 epochs

Owner

Ángel López García-Arias

An implementation of "Learning human behaviors from motion capture by adversarial imitation"

Deep Dual Consecutive Network for Human Pose Estimation (CVPR2021)

A Tensorfflow implementation of Attend, Infer, Repeat

Spectralformer: Rethinking hyperspectral image classification with transformers

Code for paper "A Critical Assessment of State-of-the-Art in Entity Alignment" (https://arxiv.org/abs/2010.16314)

Code-free deep segmentation for computational pathology

Principled Detection of Out-of-Distribution Examples in Neural Networks

Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

This repository contains the official implementation code of the paper Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis

Sequence to Sequence (seq2seq) Recurrent Neural Network (RNN) for Time Series Forecasting

Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Final project code: Implementing BicycleGAN, for CIS680 FA21 at University of Pennsylvania

This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations"

This package is for running the semantic SLAM algorithm using extracted planar surfaces from the received detection

Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes

Time should be taken seer-iously

A new test set for ImageNet

Unit-Convertor - Unit Convertor Built With Python

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