PyTorch code of my WACV 2022 paper Improving Model Generalization by Agreement of Learned Representations from Data Augmentation

Last update: Mar 04, 2022

Overview

Improving Model Generalization by Agreement of Learned Representations from Data Augmentation (WACV 2022)

Paper

ArXiv

Why it matters?

When data augmentation is applied on an input image, a model is forced to learn invariant features to improve model generalization (Figure 1).

Since data augmentation incurs little overhead, why not generate 2 data augmented images (also known as 2 positive samples) from a given input. Then, force the model to agree on the common invariant features to support the correct label (Figure 2). It turns out that maximizing this agreement further improves model model generalization. We call our method AgMax.

Unlike label smoothing, AgMax consistently improves model accuracy. For example on ImageNet1k for 90 epochs, the ResNet50 performance is as follows:

Data Augmentation	Baseline	Label Smoothing	AgMax (Ours)
Standard	76.4	76.8	76.9
CutOut	76.2	76.5	77.1
MixUp	76.5	76.7	77.6
CutMix	76.3	76.4	77.4
AutoAugment (AA)	76.2	76.2	77.1
CutOut+AA	75.7	75.7	76.6
MixUp+AA	75.9	76.5	77.1
CutMix+AA	75.5	75.5	77.0

The figure below demonstrates consistent improvement across different data augmnentation methods:

Install requirements

pip3 install -r requirements.txt

Train

For example, train ResNet50 with AgMax on 2 GPUs for 90 epochs, SGD with lr=0.1 and multistep learning rate scheduler:

CUDA_VISIBLE_DEVICES=0,1 python3 main.py --config=ResNet50-standard-agmax --train \
--multisteplr --dataset=imagenet --epochs=90 --save

Compare the results without AgMax:

CUDA_VISIBLE_DEVICES=0,1 python3 main.py --config=ResNet50-standard --train \
--multisteplr --dataset=imagenet --epochs=90 --save

Test

Using a pre-trained model:

ResNet101 trained with CutMix, AutoAugment and AgMax:

mkdir checkpoints
cd checkpoints
wget https://github.com/roatienza/agmax/releases/download/agmax-0.1.0/imagenet-agmax-mi-ResNet101-cutmix-auto_augment-81.19-mlp-4096.pth
cd ..
python3 main.py --config=ResNet101-auto_augment-cutmix-agmax --eval \
--dataset=imagenet \
--resume imagenet-agmax-mi-ResNet101-cutmix-auto_augment-81.19-mlp-4096.pth

ResNet50 trained with CutMix, AutoAugment and AgMax:

python3 main.py --config=ResNet50-auto_augment-cutmix-agmax --eval --n-units=2048 \
--dataset=imagenet --resume imagenet-agmax-ResNet50-cutmix-auto_augment-79.12-mlp-2048.pth

Other pre-trained models (Baselines):

Citation

If you find this work useful, please cite:

@inproceedings{atienza2022agmax,
  title={Improving Model Generalization by Agreement of Learned Representations from Data Augmentation},
  author={Atienza, Rowel},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year={2022},
  pubstate={published},
  tppubtype={inproceedings}
}

Comments

Question about the mutual information in the agmax loss

The agmax loss is computed by "agreement_loss, dl = agmax_loss(y, target, self.args.dl_weight)" , I do not know what does the argeement_loss and dl mean, and do not know the relationship between these two loss and the calculation of mutual information. Could you give me some help?

opened by YananGu 4

Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification (AAAI 2022) Prerequisite PyTorch = 1.2.0 P

16 Dec 14, 2022

Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).

Relation Prediction as an Auxiliary Training Objective for Knowledge Base Completion This repo provides the code for the paper Relation Prediction as

85 Jan 2, 2023

Sharpness-Aware Minimization for Efficiently Improving Generalization

Sharpness-Aware-Minimization-TensorFlow This repository provides a minimal implementation of sharpness-aware minimization (SAM) (Sharpness-Aware Minim

54 Dec 8, 2022

ImageNet-CoG is a benchmark for concept generalization. It provides a full evaluation framework for pre-trained visual representations which measure how well they generalize to unseen concepts.

The ImageNet-CoG Benchmark Project Website Paper (arXiv) Code repository for the ImageNet-CoG Benchmark introduced in the paper "Concept Generalizatio

23 Oct 9, 2022

The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022

DG-TrajGen The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022. Our Meth

25 Sep 26, 2022

Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on [ Paper ] [ Project Page ] This repository contains the code fo

97 Dec 13, 2022

This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)"

Gait3D-Benchmark This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild

82 Jan 4, 2023

Imposter-detector-2022 - HackED 2022 Team 3IQ - 2022 Imposter Detector

HackED 2022 Team 3IQ - 2022 Imposter Detector By Aneeljyot Alagh, Curtis Kan, Jo

3 Aug 20, 2022

[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints Official implementation for Reducing Footskate in Human Motion Recon

38 Nov 1, 2022

PyTorch code of my WACV 2022 paper Improving Model Generalization by Agreement of Learned Representations from Data Augmentation

Related tags

Overview

Improving Model Generalization by Agreement of Learned Representations from Data Augmentation (WACV 2022)

Paper

Why it matters?

Install requirements

Train

Test

Citation

You might also like...

Code for the AAAI-2022 paper: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).

Sharpness-Aware Minimization for Efficiently Improving Generalization

ImageNet-CoG is a benchmark for concept generalization. It provides a full evaluation framework for pre-trained visual representations which measure how well they generalize to unseen concepts.

The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022

Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)"

Imposter-detector-2022 - HackED 2022 Team 3IQ - 2022 Imposter Detector

[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Comments

Question about the mutual information in the agmax loss

Releases(agmax-0.1.0)

agmax-0.1.0(Oct 21, 2021)

Owner

Rowel Atienza

AI-Fitness-Tracker - AI Fitness Tracker With Python

Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)

Object-aware Contrastive Learning for Debiased Scene Representation

Spatial Intention Maps for Multi-Agent Mobile Manipulation (ICRA 2021)

PyTorch Code for the paper "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"

This repository provides some of the code implemented and the data used for the work proposed in "A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems".

Контрольная работа по математическим методам машинного обучения

Uni-Fold: Training your own deep protein-folding models.

Multi-Horizon-Forecasting-for-Limit-Order-Books

structured-generative-modeling

Classification of EEG data using Deep Learning

PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds

Highly comparative time-series analysis

Hand tracking demo for DIY Smart Glasses with a remote computer doing the work

This library is a location of the LegacyLogger for PyTorch Lightning.

Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

PSTR: End-to-End One-Step Person Search With Transformers (CVPR2022)

ML-Decoder: Scalable and Versatile Classification Head

The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store development.

[TNNLS 2021] The official code for the paper "Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement"