Unsupervised Representation Learning by Invariance Propagation

Last update: Jul 06, 2022

Related tags

Deep Learning InvariancePropagation

Overview

Unsupervised Learning by Invariance Propagation

This repository is the official implementation of Unsupervised Learning by Invariance Propagation.

Pretraining on Natual Images

Train on ImageNet

To train the model(s) in the paper, run this command:

python main.py --exp 'your_path' --n_background 4096 --t 0.2 --blur --cos --network 'resnet50' --nonlinearhead 1 --weight_decay 1e-4

Evaluation

To evaluate the model on ImageNet, run:

python -m downstream.linear_classification.linear_classification --gpus '0,1' --exp 'your_exp_path' --pretrained_path 'pretrain_path' --backbone 'resnet50'

Notice that in the paper, to calculate the BFS results, we require to record the id of neighbours of each anchor point. For computational efficiency, we apprximate the BFS results by only concatenating the neighbours of each point, up to L steps. This results may be a little different with the real BFS results due to there exists repeated samples, however it works pretty well, both effectively and efficiently. Pretrained model can be found here.

Train on Cifar

To train the model(s) in cifar10 and cifar100 or svhn, run this command:

# cifar10
python main.py --exp 'your_path' -n_background 4096 --t 0.2 --blur --cos --network 'resnet18_cifar' --nonlinearhead 1 --weight_decay 5e-4 --n_pos 20 --dataset 'cifar10'
# cifar100
python main.py --exp 'your_path' -n_background 4096 --t 0.2 --blur --cos --network 'resnet18_cifar' --nonlinearhead 1 --weight_decay 5e-4 --n_pos 20 --dataset 'cifar100'
# svhn
python main.py --exp 'your_path' -n_background 4096 --t 0.2 --blur --cos --network 'resnet18_cifar' --nonlinearhead 1 --weight_decay 5e-4 --n_pos 20 --dataset 'svhn'

Evaluation

To train the model(s) in cifar10 and cifar100 run this command:

# cifar10
python -m downstream.linear_classification.eval_linear --gpus '0,1' --exp 'your_exp_path' --pretrained_path 'pretrain_path' --backbone 'resnet18_cifar' --dataset 'cifar10'
# cifar100
python -m downstream.linear_classification.eval_linear --gpus '0,1' --exp 'your_exp_path' --pretrained_path 'pretrain_path' --backbone 'resnet18_cifar' --dataset 'cifar100'
# svhn
python -m downstream.linear_classification.eval_linear --gpus '0,1' --exp 'your_exp_path' --pretrained_path 'pretrain_path' --backbone 'resnet18_cifar' --dataset 'svhn'

Pretraining on Defect Classification Dataset

For validate the effectiveness and practicabilities of the proposed algorithms, we can also train and evaluate our method on Defect Detection Dataset.

Train on WM811.

python main.py --gpus '0,1,2' --exp 'output/' --n_background 4096 --t 0.07 --cos --network 'resnet18_wm811' --dataset 'wm811' --nonlinearhead 0 --weight_decay 5e-4

Evaluation

To evaluate the model on WM811, run:

python -m downstream.fine_tune_wm811 --save_folder 'your_output_folder' --model_path 'your_pretrain_model' --model 'resnet18_wm811' --dataset 'wm811' --weight_decay 1e-3 --learning_rate1 0.001 --learning_rate2 0.002 --label_smoothing 0.1 --dropout 0.5

Unsupervised Representation Learning by Invariance Propagation

Related tags

Overview

Unsupervised Learning by Invariance Propagation

Pretraining on Natual Images

Train on ImageNet

Evaluation

Train on Cifar

Evaluation

Pretraining on Defect Classification Dataset

Train on WM811.

Evaluation

Owner

FengWang

Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems

ICS 4u HD project, start before-wards. A curtain shooting game using python.

Constructing Neural Network-Based Models for Simulating Dynamical Systems

A high performance implementation of HDBSCAN clustering.

Automatic Differentiation Multipole Moment Molecular Forcefield

Dynamic View Synthesis from Dynamic Monocular Video

Python scripts performing class agnostic object localization using the Object Localization Network model in ONNX.

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Self-Supervised Pillar Motion Learning for Autonomous Driving (CVPR 2021)

PyTorch Implementation of Vector Quantized Variational AutoEncoders.

Uncertain natural language inference

[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

Code for “ACE-HGNN: Adaptive Curvature ExplorationHyperbolic Graph Neural Network”

Half Instance Normalization Network for Image Restoration

Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation (CVPR 2020)

maximal update parametrization (µP)

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

The aim of the game, as in the original one, is to find a specific image from a group of different images of a person's face

An intuitive library to extract features from time series