[ICML'21] Estimate the accuracy of the classifier in various environments through self-supervision

Overview

What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?

[Paper] [ICML'21 Project]

PyTorch Implementation

This repository contains:

  • the PyTorch implementation of AutoEavl.
  • the example on CIFAR-10 setup (use imgaug)
  • linear regression

Please follow the instruction below to install it and run the experiment demo.

Prerequisites

  • Linux (tested on Ubuntu 16.04LTS)
  • NVIDIA GPU + CUDA CuDNN (tested on GTX 2080 Ti)
  • CIFAR-10 (download and unzip to PROJECT_DIR/data/)
  • CIFAR10.1 (download and unzip to PROJECT_DIR/data/CIFAR-10.1)
  • Please use PyTorch1.5 to avoid compilation errors (other versions should be good)
  • You might need to change the file paths, and please be sure you change the corresponding paths in the codes as well

Getting started

  1. Install dependencies
    # Imgaug (or see https://imgaug.readthedocs.io/en/latest/source/installation.html)
    conda config --add channels conda-forge
    conda install imgaug
  2. Creat synthetic sets
    # By default it creates 500 synthetic sets
    python generate_synthetic_sets.py
  3. Learn classifier on CIFAR-10 (DenseNet-10-12)
    # Save as "PROJECT_DIR/DenseNet-40-12-ss/checkpoint.pth.tar"
    # Modified based on the wonderful github of https://github.com/andreasveit/densenet-pytorch
    python train.py --layers 40 --growth 12 --no-bottleneck --reduce 1.0
  4. Test classifier on synthetic sets
    # 1) Get "PROJECT_DIR/accuracy_cls_dense_aug.npy" file
    # 2) Get "PROJECT_DIR/accuracy_ss_dense_aug.npy" file
    # 3) You will see Rank correlation and Pearsons correlation
    # 4) The absolute error of linear regression is also shown
    python test_many.py --layers 40 --growth 12 --no-bottleneck --reduce 1.0
  5. Correlation study
    # You will see correlation.pdf;
    python analyze_correlation.py
        

Citation

If you use the code in your research, please cite:

    @inproceedings{Deng:ICML2021,
      author    = {Weijian Deng and
                   Stephen Gould and
                   Liang Zheng},
      title     = {What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?},
      booktitle = {ICML},
      year      = {2021}
    }

License

MIT

Owner
Third-year PhD student at ANU.
Implementing Vision Transformer (ViT) in PyTorch

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

2 Dec 24, 2021
This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

3D-CVF This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object

YecheolKim 97 Dec 20, 2022
Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt) Task Training huge unsupervised deep neural networks yields to strong progress in

2 Aug 05, 2022
MIMO-UNet - Official Pytorch Implementation

MIMO-UNet - Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Rethinking Coarse-to-

Sungjin Cho 248 Jan 02, 2023
My implementation of transformers related papers for computer vision in pytorch

vision_transformers This is my personnal repo to implement new transofrmers based and other computer vision DL models I am currenlty working without a

samsja 1 Nov 10, 2021
Customised to detect objects automatically by a given model file(onnx)

LabelImg LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Annotations are saved as XML

Heeone Lee 1 Jun 07, 2022
MiniSom is a minimalistic implementation of the Self Organizing Maps

MiniSom Self Organizing Maps MiniSom is a minimalistic and Numpy based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial N

Giuseppe Vettigli 1.2k Jan 03, 2023
My implementation of Image Inpainting - A deep learning Inpainting model

Image Inpainting What is Image Inpainting Image inpainting is a restorative process that allows for the fixing or removal of unwanted parts within ima

Joshua V Evans 1 Dec 12, 2021
Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks.

The Lottery Ticket Hypothesis for Pre-trained BERT Networks Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks. [NeurIPS

VITA 122 Dec 14, 2022
HAR-stacked-residual-bidir-LSTMs - Deep stacked residual bidirectional LSTMs for HAR

HAR-stacked-residual-bidir-LSTM The project is based on this repository which is presented as a tutorial. It consists of Human Activity Recognition (H

Guillaume Chevalier 287 Dec 27, 2022
Super Pix Adv - Offical implemention of Robust Superpixel-Guided Attentional Adversarial Attack (CVPR2020)

Super_Pix_Adv Offical implemention of Robust Superpixel-Guided Attentional Adver

DLight 8 Oct 26, 2022
"Neural Turing Machine" in Tensorflow

Neural Turing Machine in Tensorflow Tensorflow implementation of Neural Turing Machine. This implementation uses an LSTM controller. NTM models with m

Taehoon Kim 1k Dec 06, 2022
[ACM MM 2021] TSA-Net: Tube Self-Attention Network for Action Quality Assessment

Tube Self-Attention Network (TSA-Net) This repository contains the PyTorch implementation for paper TSA-Net: Tube Self-Attention Network for Action Qu

ShunliWang 18 Dec 23, 2022
The versatile ocean simulator, in pure Python, powered by JAX.

Veros is the versatile ocean simulator -- it aims to be a powerful tool that makes high-performance ocean modeling approachable and fun. Because Veros

TeamOcean 245 Dec 20, 2022
Bolt Online Learning Toolbox

Bolt Online Learning Toolbox Bolt features discriminative learning of linear predictors (e.g. SVM or Logistic Regression) using fast online learning a

Peter Prettenhofer 87 Dec 12, 2022
Estimation of human density in a closed space using deep learning.

Siemens HOLLZOF challenge - Human Density Estimation Add project description here. Installing Dependencies: Install Python3 either system-wide, user-w

3 Aug 08, 2021
TC-GNN with Pytorch integration

TC-GNN (Running Sparse GNN on Dense Tensor Core on Ampere GPU) Cite this project and paper. @inproceedings{TC-GNN, title={TC-GNN: Accelerating Spars

YUKE WANG 19 Dec 01, 2022
A toolset of Python programs for signal modeling and indentification via sparse semilinear autoregressors.

SPAAR Description A toolset of Python programs for signal modeling via sparse semilinear autoregressors. References Vides, F. (2021). Computing Semili

Fredy Vides 0 Oct 30, 2021
Ontologysim: a Owlready2 library for applied production simulation

Ontologysim: a Owlready2 library for applied production simulation Ontologysim is an open-source deep production simulation framework, with an emphasi

10 Nov 30, 2022
Bayesian Image Reconstruction using Deep Generative Models

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Razvan Valentin Marinescu 51 Nov 23, 2022