ReAct: Out-of-distribution Detection With Rectified Activations

Related tags

Deep Learningreact
Overview

ReAct: Out-of-distribution Detection With Rectified Activations

This is the source code for paper ReAct: Out-of-distribution Detection With Rectified Activations by Yiyou Sun, Chuan Guo and Yixuan Li.

In this work, we propose ReAct—a simple technique for reducing model overconfidence on OOD data. Our method is motivated by novel analysis on internal activations of neural networks, which displays highly distinctive signature patterns for most OOD distributions.

Usage

1. Dataset Preparation

In-distribution dataset

Please download ImageNet-1k and place the training data and validation data in ./datasets/id_data/ILSVRC-2012/train and ./datasets/id_data/ILSVRC-2012/val, respectively.

Out-of-distribution dataset

We have curated 4 OOD datasets from iNaturalist, SUN, Places, and Textures, and de-duplicated concepts overlapped with ImageNet-1k.

For iNaturalist, SUN, and Places, we have sampled 10,000 images from the selected concepts for each dataset, which can be download via the following links:

wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/iNaturalist.tar.gz
wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/SUN.tar.gz
wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/Places.tar.gz

For Textures, we use the entire dataset, which can be downloaded from their original website.

Please put all downloaded OOD datasets into ./datasets/ood_data/.

2. Pre-trained Model Preparation

The model we used in the paper is the pre-trained ResNet-50 and MobileNet-v2 provided by Pytorch. The download process will start upon running.

3. OOD Detection Evaluation

To reproduce our results on ResNet-50, please run:

python eval.py --threshold 1.0

To reproduce baseline approaches (Energy Score), please run:

python eval.py --threshold 1e6  #we set the threshold close to infinity, so it is the original energy score.

OOD Detection Results

ReACT achieves state-of-the-art performance averaged on the 4 OOD datasets.

results

Citation

If you use our codebase, please cite our work:

@inproceedings{sun2021react,
  title={ReAct: Out-of-distribution Detection With Rectified Activations},
  author={Sun, Yiyou and Guo, Chuan and Li, Yixuan},
  booktitle={Advances in Neural Information Processing Systems},
  year={2021}
}
Owner
CS Research Group led by Prof. Sharon Li
BirdCLEF 2021 - Birdcall Identification 4th place solution

BirdCLEF 2021 - Birdcall Identification 4th place solution My solution detail kaggle discussion Inference Notebook (best submission) Environment Use K

tattaka 42 Jan 02, 2023
The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal Transport Maps, ICLR 2022.

Generative Modeling with Optimal Transport Maps The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal

Litu Rout 30 Dec 22, 2022
Deep Learning Pipelines for Apache Spark

Deep Learning Pipelines for Apache Spark The repo only contains HorovodRunner code for local CI and API docs. To use HorovodRunner for distributed tra

Databricks 2k Jan 08, 2023
Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images

Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images This repository contains the implementation of the following paper

Seonggwan Ko 9 Jul 30, 2022
Oscar and VinVL

Oscar: Object-Semantics Aligned Pre-training for Vision-and-Language Tasks VinVL: Revisiting Visual Representations in Vision-Language Models Updates

Microsoft 938 Dec 26, 2022
Code release for DS-NeRF (Depth-supervised Neural Radiance Fields)

Depth-supervised NeRF: Fewer Views and Faster Training for Free Project | Paper | YouTube Pytorch implementation of our method for learning neural rad

524 Jan 08, 2023
RRxIO - Robust Radar Visual/Thermal Inertial Odometry: Robust and accurate state estimation even in challenging visual conditions.

RRxIO - Robust Radar Visual/Thermal Inertial Odometry RRxIO offers robust and accurate state estimation even in challenging visual conditions. RRxIO c

Christopher Doer 64 Dec 29, 2022
codes for "Scheduled Sampling Based on Decoding Steps for Neural Machine Translation" (long paper of EMNLP-2022)

Scheduled Sampling Based on Decoding Steps for Neural Machine Translation (EMNLP-2021 main conference) Contents Overview Background Quick to Use Furth

Adaxry 13 Jul 25, 2022
Self-Supervised Contrastive Learning of Music Spectrograms

Self-Supervised Music Analysis Self-Supervised Contrastive Learning of Music Spectrograms Dataset Songs on the Billboard Year End Hot 100 were collect

27 Dec 10, 2022
Official implementation of SIGIR'2021 paper: "Sequential Recommendation with Graph Neural Networks".

SURGE: Sequential Recommendation with Graph Neural Networks This is our TensorFlow implementation for the paper: Sequential Recommendation with Graph

FIB LAB, Tsinghua University 53 Dec 26, 2022
Official Implementation of SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

Official Implementation of SimIPU SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations Since

Zhyever 37 Dec 01, 2022
YoHa - A practical hand tracking engine.

YoHa - A practical hand tracking engine.

2k Jan 06, 2023
💊 A 3D Generative Model for Structure-Based Drug Design (NeurIPS 2021)

A 3D Generative Model for Structure-Based Drug Design Coming soon... Citation @inproceedings{luo2021sbdd, title={A 3D Generative Model for Structu

Shitong Luo 118 Jan 05, 2023
A python library for time-series smoothing and outlier detection in a vectorized way.

tsmoothie A python library for time-series smoothing and outlier detection in a vectorized way. Overview tsmoothie computes, in a fast and efficient w

Marco Cerliani 517 Dec 28, 2022
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness This repository contains the code used for the exper

H.R. Oosterhuis 28 Nov 29, 2022
基于Paddle框架的arcface复现

arcface-Paddle 基于Paddle框架的arcface复现 ArcFace-Paddle 本项目基于paddlepaddle框架复现ArcFace,并参加百度第三届论文复现赛,将在2021年5月15日比赛完后提供AIStudio链接~敬请期待 参考项目: InsightFace Padd

QuanHao Guo 16 Dec 15, 2022
Rational Activation Functions - Replacing Padé Activation Units

Rational Activations - Learnable Rational Activation Functions First introduce as PAU in Padé Activation Units: End-to-end Learning of Activation Func

<a href=[email protected]"> 38 Nov 22, 2022
Libtorch yolov3 deepsort

Overview It is for my undergrad thesis in Tsinghua University. There are four modules in the project: Detection: YOLOv3 Tracking: SORT and DeepSORT Pr

Xu Wei 226 Dec 13, 2022
[CVPR 2021] Forecasting the panoptic segmentation of future video frames

Panoptic Segmentation Forecasting Colin Graber, Grace Tsai, Michael Firman, Gabriel Brostow, Alexander Schwing - CVPR 2021 [Link to paper] We propose

Niantic Labs 44 Nov 29, 2022