Pytorch implementation of Learning Rate Dropout.

Last update: Nov 25, 2022

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Deep Learning Learning-Rate-Dropout

Overview

Learning-Rate-Dropout

Pytorch implementation of Learning Rate Dropout.

Paper Link: https://arxiv.org/pdf/1912.00144.pdf

Train ResNet-34 for Cifar10:

run:

python main.py --model=resnet --optim=adam_lrd --lr=0.001 --LRD_p=0.5

python main.py --model=resnet --optim=adam --lr=0.001

python main.py --model=resnet --optim=sgd_lrd --lr=0.1 --LRD_p=0.5

python main.py --model=resnet --optim=sgd --lr=0.1 

python main.py --model=resnet --optim=rmsprop_lrd --lr=0.001 --LRD_p=0.5

python main.py --model=resnet --optim=rmsprop --lr=0.001

After training, run "plot.py" to show the learning curves.

Owner

GitHub Repository

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Pytorch implementation of Learning Rate Dropout.

Related tags

Overview

Learning-Rate-Dropout

Owner

Swin-Transformer is basically a hierarchical Transformer whose representation is computed with shifted windows.

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020).

Breaking the Dilemma of Medical Image-to-image Translation

Code for Mining the Benefits of Two-stage and One-stage HOI Detection

Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models

Rethinking Transformer-based Set Prediction for Object Detection

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

Fast and Simple Neural Vocoder, the Multiband RNNMS

Prior-Guided Multi-View 3D Head Reconstruction

[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

Official implementation of the ICLR 2021 paper

Code for NeurIPS 2021 paper "Curriculum Offline Imitation Learning"

Official code for article "Expression is enough: Improving traﬀic signal control with advanced traﬀic state representation"

PyTorch implementation of the Pose Residual Network (PRN)

[CVPR2021] The source code for our paper 《Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning》.

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