[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.
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition

TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition Xue, Wenyuan, et al. "TGRNet: A Table Graph Reconstruction Network for Ta

Wenyuan 68 Jan 04, 2023
A TikTok-like recommender system for GitHub repositories based on Gorse

GitRec GitRec is the missing recommender system for GitHub repositories based on Gorse. Architecture The trending crawler crawls trending repositories

337 Jan 04, 2023
Code for "Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency" paper

UNICORN 🦄 Webpage | Paper | BibTex PyTorch implementation of "Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency" pap

118 Jan 06, 2023
SynNet - synthetic tree generation using neural networks

SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s

Wenhao Gao 60 Dec 29, 2022
⚖️🔁🔮🕵️‍♂️🦹🖼️ Code for *Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances* paper.

Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances This repository contains the code for Measuring the Co

Daniel Steinberg 0 Nov 06, 2022
[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yan

Ayan Kumar Bhunia 44 Dec 12, 2022
EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.

This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. Please contact me at

9 Oct 28, 2022
Auxiliary Raw Net (ARawNet) is a ASVSpoof detection model taking both raw waveform and handcrafted features as inputs, to balance the trade-off between performance and model complexity.

Overview This repository is an implementation of the Auxiliary Raw Net (ARawNet), which is ASVSpoof detection system taking both raw waveform and hand

6 Jul 08, 2022
The codes and models in 'Gaze Estimation using Transformer'.

GazeTR We provide the code of GazeTR-Hybrid in "Gaze Estimation using Transformer". We recommend you to use data processing codes provided in GazeHub.

65 Dec 27, 2022
The mini-AlphaStar (mini-AS, or mAS) - mini-scale version (non-official) of the AlphaStar (AS)

A mini-scale reproduction code of the AlphaStar program. Note: the original AlphaStar is the AI proposed by DeepMind to play StarCraft II.

Ruo-Ze Liu 216 Jan 04, 2023
sktime companion package for deep learning based on TensorFlow

NOTE: sktime-dl is currently being updated to work correctly with sktime 0.6, and wwill be fully relaunched over the summer. The plan is Refactor and

sktime 573 Jan 05, 2023
A pytorch reprelication of the model-based reinforcement learning algorithm MBPO

Overview This is a re-implementation of the model-based RL algorithm MBPO in pytorch as described in the following paper: When to Trust Your Model: Mo

Xingyu Lin 93 Jan 05, 2023
Lazy, a tool for running things in idle time

Lazy, a tool for running things in idle time Mostly used to stop CUDA ML model training from making my desktop unusable. Simply monitors keyboard/mous

N Shepperd 46 Nov 06, 2022
TinyML Cookbook, published by Packt

TinyML Cookbook This is the code repository for TinyML Cookbook, published by Packt. Author: Gian Marco Iodice Publisher: Packt About the book This bo

Packt 93 Dec 29, 2022
Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

This is a Pytorch implementation of Janai, J., Güney, F., Ranjan, A., Black, M. and Geiger, A., Unsupervised Learning of Multi-Frame Optical Flow with

Anurag Ranjan 110 Nov 02, 2022
BMN: Boundary-Matching Network

BMN: Boundary-Matching Network A pytorch-version implementation codes of paper: "BMN: Boundary-Matching Network for Temporal Action Proposal Generatio

qinxin 260 Dec 06, 2022
This is a five-step framework for the development of intrusion detection systems (IDS) using machine learning (ML) considering model realization, and performance evaluation.

AB-TRAP: building invisibility shields to protect network devices The AB-TRAP framework is applicable to the development of Network Intrusion Detectio

Lab-C2DC - Laboratory of Command and Control and Cyber-security 17 Jan 04, 2023
A modern pure-Python library for reading PDF files

pdf A modern pure-Python library for reading PDF files. The goal is to have a modern interface to handle PDF files which is consistent with itself and

6 Apr 06, 2022
Yolo object detection - Yolo object detection with python

How to run download required files make build_image make download Docker versio

3 Jan 26, 2022
A Keras implementation of YOLOv3 (Tensorflow backend)

keras-yolo3 Introduction A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K. Quick Start Download YOLOv3 weights fro

7.1k Jan 03, 2023