SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

Related tags

Deep LearningSSD
Overview

SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

Pdf: https://openreview.net/forum?id=v5gjXpmR8J

Code for our ICLR 2021 paper on outlier detection, titled SSD, without requiring class labels of in-distribution training data. We leverage recent advances in self-supervised representation learning followed by the cluster-based outlier detection to achieve competitive performance. This repository support both self-supervised training of networks and outlier detection evaluation of pre-trained networks. It also includes code for the two proposed extensions in the paper, i.e., 1) Few-shot outlier detection and 2) Extending SSD by including class labels, when available.

Getting started

Let's start by installing all dependencies.

pip install -r requirement.txt

Outlier detection with a pre-trained classifier

This is how we can evaluate the performance of a pre-trained ResNet50 classifier trained using SimCLR on the CIFAR-10 dataset.

CUDA_VISIBLE_DEVICES=$gpus_ids python -u eval_ssd.py --arch resnet50 --training-mode SimCLR --dataset cifar10 --ckpt checkpoint_path --normalize --exp-name name_of_this_experiment

  • training-mode: Choose from ("SimCLR", "SupCon", "SupCE"). This will choose the right network modules for the checkpoint.
  • arch: Choose from available architectures in models.py
  • dataset: Choose from ("cifar10", "cifar100", "svhn", "stl")
  • --normalize: If set, it will normalize input images. Use only if inputs were normalized in training too.
  • --exp-name: Experiment name. We will log results into a text file of this name.

The steps to evaluate with $SSD_k$ are exactly the same, except that now you have to also provide values for k and copies . k refers to how many outliers are available from each class of targeted OOD datasets while copies refers to the number of transformed instances created per available outlier image.

CUDA_VISIBLE_DEVICES=$gpu_id python -u eval_ssdk.py --arch resnet50 --training-mode SimCLR --dataset cifar10 --ckpt checkpoint_path --normalize --k 5 --copies 10

Training a classifier using self-supervised/supervised learning

We also support training a classifier using self-supervised, supervised or a combination of both training methods. Here is an example script to train a ResNet50 network on the CIFAR-10 dataset using SimCLR.

CUDA_VISIBLE_DEVICES=$gpus_ids python -u train.py --arch resnet50 --training-mode SimCLR --dataset cifar10 --results-dir directory_to_save_checkpoint --exp-name name_of_this_experiment --warmup --normalize

  • --training-mode: Choose from ("SimCLR", "SupCon", "SupCE"). This will choose appropriate network modules, loss functions, and trainers.
  • --warmup: We recommend using warmup when batch-size is large, which is often the case for self-supervised methods.

Choices for other arguments are similar to what we mentioned earlier in the evaluation section.

Reference

If you find this work helpful, consider citing it.

@inproceedings{sehwag2021ssd,
  title={SSD:  A Unified Framework for Self-Supervised Outlier Detection},
  author={Vikash Sehwag and Mung Chiang and Prateek Mittal},
 booktitle={International Conference on Learning Representations},
 year={2021},
 url={https://openreview.net/forum?id=v5gjXpmR8J}
}
Convert Mission Planner (ArduCopter) Waypoint Missions to Litchi CSV Format to execute on DJI Drones

Mission Planner to Litchi Convert Mission Planner (ArduCopter) Waypoint Surveys to Litchi CSV Format to execute on DJI Drones Litchi doesn't support S

Yaros 24 Dec 09, 2022
L-Verse: Bidirectional Generation Between Image and Text

Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalabilty

Kim, Taehoon 102 Dec 21, 2022
Localizing Visual Sounds the Hard Way

Localizing-Visual-Sounds-the-Hard-Way Code and Dataset for "Localizing Visual Sounds the Hard Way". The repo contains code and our pre-trained model.

Honglie Chen 58 Dec 07, 2022
Implementation of UNET architecture for Image Segmentation.

Semantic Segmentation using UNET This is the implementation of UNET on Carvana Image Masking Kaggle Challenge About the Dataset This dataset contains

Anushka agarwal 4 Dec 21, 2021
SelfRemaster: SSL Speech Restoration

SelfRemaster: Self-Supervised Speech Restoration Official implementation of SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesi

Takaaki Saeki 46 Jan 07, 2023
PixelPyramids: Exact Inference Models from Lossless Image Pyramids (ICCV 2021)

PixelPyramids: Exact Inference Models from Lossless Image Pyramids This repository contains the PyTorch implementation of the paper PixelPyramids: Exa

Visual Inference Lab @TU Darmstadt 8 Dec 11, 2022
Code for the ICCV 2021 paper "Pixel Difference Networks for Efficient Edge Detection" (Oral).

Microsoft365_devicePhish Abusing Microsoft 365 OAuth Authorization Flow for Phishing Attack This is a simple proof-of-concept script that allows an at

Alex 236 Dec 21, 2022
Experiments for Fake News explainability project

fake-news-explainability Experiments for fake news explainability project This repository only contains the notebooks used to train the models and eva

Lorenzo Flores (Lj) 1 Dec 03, 2022
Code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2021

The repo provides the code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2

Yuning Mao 18 May 24, 2022
DeepLab resnet v2 model in pytorch

pytorch-deeplab-resnet DeepLab resnet v2 model implementation in pytorch. The architecture of deepLab-ResNet has been replicated exactly as it is from

Isht Dwivedi 601 Dec 22, 2022
Easy-to-use micro-wrappers for Gym and PettingZoo based RL Environments

SuperSuit introduces a collection of small functions which can wrap reinforcement learning environments to do preprocessing ('microwrappers'). We supp

Farama Foundation 357 Jan 06, 2023
Simple-Neural-Network From Scratch in Python

Simple-Neural-Network From Scratch in Python This is a simple Neural Network created without any Machine Learning Libraries. The only dependencies are

Aum Shah 1 Dec 28, 2021
Finetune alexnet with tensorflow - Code for finetuning AlexNet in TensorFlow >= 1.2rc0

Finetune AlexNet with Tensorflow Update 15.06.2016 I revised the entire code base to work with the new input pipeline coming with TensorFlow = versio

Frederik Kratzert 766 Jan 04, 2023
[ICCV '21] In this repository you find the code to our paper Keypoint Communities

Keypoint Communities In this repository you will find the code to our ICCV '21 paper: Keypoint Communities Duncan Zauss, Sven Kreiss, Alexandre Alahi,

Duncan Zauss 262 Dec 13, 2022
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

PEPit: Performance Estimation in Python This open source Python library provides a generic way to use PEP framework in Python. Performance estimation

Baptiste 53 Nov 16, 2022
Jittor Medical Segmentation Lib -- The assignment of Pattern Recognition course (2021 Spring) in Tsinghua University

THU模式识别2021春 -- Jittor 医学图像分割 模型列表 本仓库收录了课程作业中同学们采用jittor框架实现的如下模型: UNet SegNet DeepLab V2 DANet EANet HarDNet及其改动HarDNet_alter PSPNet OCNet OCRNet DL

48 Dec 26, 2022
Driller: augmenting AFL with symbolic execution!

Driller Driller is an implementation of the driller paper. This implementation was built on top of AFL with angr being used as a symbolic tracer. Dril

Shellphish 791 Jan 06, 2023
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

DOC | Quick Start | 中文 Breaking News !! 🔥 🔥 🔥 OGB-LSC KDD CUP 2021 winners announced!! (2021.06.17) Super excited to announce our PGL team won TWO

1.5k Jan 06, 2023
This repository contains the source code of our work on designing efficient CNNs for computer vision

Efficient networks for Computer Vision This repo contains source code of our work on designing efficient networks for different computer vision tasks:

Sachin Mehta 386 Nov 26, 2022
🛰️ List of earth observation companies and job sites

Earth Observation Companies & Jobs source Portals & Jobs Geospatial Geospatial jobs newsletter: ~biweekly newsletter with geospatial jobs by Ali Ahmad

Dahn 64 Dec 27, 2022