We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Last update: Sep 08, 2022

Related tags

Overview

This repository provides the implementation for the paper "Improved Regularization and Robustness for Fine-tuning in Neural Networks", which will be presented as a poster paper in NeurIPS'21.

In this work, we propose a regularized self-labeling approach that combines regularization and self-training methods for improving the generalization and robustness properties of fine-tuning. Our approach includes two components:

First, we encode layer-wise regularization to penalize the model weights at different layers of the neural net.
Second, we add self-labeling that relabels data points based on current neural net's belief and reweights data points whose confidence is low.

Requirements

To install requirements:

pip install -r requirements.txt

Data Preparation

We use seven image datasets in our paper. We list the link for downloading these datasets and describe how to prepare data to run our code below.

Aircrafts: download and extract into ./data/aircrafts
- remove the class 257.clutter out of the data directory
CUB-200-2011: download and extract into ./data/CUB_200_2011/
Caltech-256: download and extract into ./data/caltech256/
Stanford-Cars: download and extract into ./data/StanfordCars/
Stanford-Dogs: download and extract into ./data/StanfordDogs/
Flowers: download and extract into ./data/flowers/
MIT-Indoor: download and extract into ./data/Indoor/

Our code automatically handles the split of the datasets.

Usage

Our algorithm (RegSL) interpolates between layer-wise regularization and self-labeling. Run the following commands for conducting experiments in this paper.

Fine-tuning ResNet-101 on image classification tasks.

python train_constraint.py --model ResNet101 \
    --config configs/config_constraint_indoor.json \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 0.136809975858091 --reg_predictor 6.40780158171339 --scale_factor 2.52883770643206\
    --device 1

python train_constraint.py --model ResNet101 \
    --config configs/config_constraint_aircrafts.json \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 1.18330556653284 --reg_predictor 5.27713618808711 --scale_factor 1.27679969876201\
    --device 1

python train_constraint.py --model ResNet101 \
    --config configs/config_constraint_birds.json \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 0.204403908747731 --reg_predictor 23.7850606577679 --scale_factor 4.73803591794678\
    --device 1

python train_constraint.py --model ResNet101 \
    --config configs/config_constraint_caltech.json \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 0.0867998872549272 --reg_predictor 9.4552942790218 --scale_factor 1.1785989596144\
    --device 1

python train_constraint.py --model ResNet101 \
    --config configs/config_constraint_cars.json \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 1.3340347414257 --reg_predictor 8.26940794089601 --scale_factor 3.47676759842434\
    --device 1

python train_constraint.py --model ResNet101 \
    --config configs/config_constraint_dogs.json \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 0.0561320847651626 --reg_predictor 4.46281825974388 --scale_factor 1.58722606909531\
    --device 1

python train_constraint.py --model ResNet101 \
    --config configs/config_constraint_flower.json \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 0.131991042311165 --reg_predictor 10.7674132173309 --scale_factor 4.98010215976503\
    --device 1

Fine-tuning ResNet-18 under label noise.

python train_label_noise.py --config configs/config_constraint_indoor.json --model ResNet18 \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 7.80246991703043 --reg_predictor 14.077402847906 \
    --noise_rate 0.2 --train_correct_label --reweight_epoch 5 --reweight_temp 2.0 --correct_epoch 10 --correct_thres 0.9 

python train_label_noise.py --config configs/config_constraint_indoor.json --model ResNet18 \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 8.47139398080791 --reg_predictor 19.0191127114923 \
    --noise_rate 0.4 --train_correct_label --reweight_epoch 5 --reweight_temp 2.0 --correct_epoch 10 --correct_thres 0.9 

python train_label_noise.py --config configs/config_constraint_indoor.json --model ResNet18 \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 10.7576018531961 --reg_predictor 19.8157649727473 \
    --noise_rate 0.6 --train_correct_label --reweight_epoch 5 --reweight_temp 2.0 --correct_epoch 10 --correct_thres 0.9 
    
python train_label_noise.py --config configs/config_constraint_indoor.json --model ResNet18 \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 9.2031662757248 --reg_predictor 6.41568500472423 \
    --noise_rate 0.8 --train_correct_label --reweight_epoch 5 --reweight_temp 1.5 --correct_epoch 10 --correct_thres 0.9

Fine-tuning Vision Transformer on noisy labels.

python train_label_noise.py --config configs/config_constraint_indoor.json \
    --model VisionTransformer --is_vit --img_size 224 --vit_type ViT-B_16 --vit_pretrained_dir pretrained/imagenet21k_ViT-B_16.npz \
    --reg_method none --reg_norm none \
    --lr 0.0001 --device 1 --noise_rate 0.4

python train_label_noise.py --config configs/config_constraint_indoor.json \
    --model VisionTransformer --is_vit --img_size 224 --vit_type ViT-B_16 --vit_pretrained_dir pretrained/imagenet21k_ViT-B_16.npz \
    --reg_method none --reg_norm none \
    --lr 0.0001 --device 1 --noise_rate 0.8

python train_label_noise.py --config configs/config_constraint_indoor.json \
    --model VisionTransformer --is_vit --img_size 224 --vit_type ViT-B_16 --vit_pretrained_dir pretrained/imagenet21k_ViT-B_16.npz \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 0.7488074175044196 --reg_predictor 9.842955837419588 \
    --train_correct_label --reweight_epoch 24 --correct_epoch 18\
    --lr 0.0001 --device 1 --noise_rate 0.4

python train_label_noise.py --config configs/config_constraint_indoor.json \
    --model VisionTransformer --is_vit --img_size 224 --vit_type ViT-B_16 --vit_pretrained_dir pretrained/imagenet21k_ViT-B_16.npz \
    --reg_method constraint --reg_norm frob \
    --reg_extractor 0.1568903647089986 --reg_predictor 1.407080880079702 \
    --train_correct_label --reweight_epoch 18 --correct_epoch 2\
    --lr 0.0001 --device 1 --noise_rate 0.8

Please follow the instructions in ViT-pytorch to download the pre-trained models.

Fine-tuning ResNet-18 on ChestX-ray14 data set.

Run experiments on ChestX-ray14 in reproduce-chexnet path:

cd reproduce-chexnet

python retrain.py --reg_method None --reg_norm None --device 0

python retrain.py --reg_method constraint --reg_norm frob \
    --reg_extractor 5.728564437344309 --reg_predictor 2.5669480884876905 --scale_factor 1.0340072757925474 \
    --device 0

Citation

If you find this repository useful, consider citing our work titled above.

Acknowledgment

Thanks to the authors of the following repositories for providing their implementation publicly available.

We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

Related tags

Overview

Overview

Requirements

Data Preparation

Usage

Citation

Acknowledgment

Owner

NEU-StatsML-Research

Large scale PTM - PPI relation extraction

Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of images as "pixels"

Sentinel-1 vessel detection model used in the xView3 challenge

A simple image/video to Desmos graph converter run locally

This project demonstrates the use of neural networks and computer vision to create a classifier that interprets the Brazilian Sign Language.

Cl datasets - PyTorch image dataloaders and utility functions to load datasets for supervised continual learning

YOLOv3 in PyTorch > ONNX > CoreML > TFLite

SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches

Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training".

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Official PyTorch implementation for paper "Efficient Two-Stage Detection of Human–Object Interactions with a Novel Unary–Pairwise Transformer"

Monitora la qualità della ricezione dei segnali radio nelle province siciliane.

Multiple style transfer via variational autoencoder

Official repository for "Restormer: Efficient Transformer for High-Resolution Image Restoration". SOTA results for single-image motion deblurring, image deraining, image denoising (synthetic and real data), and dual-pixel defocus deblurring.

PyTorch implementaton of our CVPR 2021 paper "Bridging the Visual Gap: Wide-Range Image Blending"

Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

A PyTorch implementation of PointRend: Image Segmentation as Rendering

STMTrack: Template-free Visual Tracking with Space-time Memory Networks

Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face Manipulation" published in CVPR 2020.

Weighted QMIX: Expanding Monotonic Value Function Factorisation