Source code for the project OOD Detection of COVID-19 From Chest X-Ray Images, written as part of the KTH course DD2424, Deep Learning in Data Science:
-
Updated
May 20, 2020 - Python
Source code for the project OOD Detection of COVID-19 From Chest X-Ray Images, written as part of the KTH course DD2424, Deep Learning in Data Science:
Self-Supervised Learning for OOD Detection (NeurIPS 2019)
Code for Paper: Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
A list of accepted papers in AAAI 2021 about anomaly detection.
[SafeAI'21] Feature Space Singularity for Out-of-Distribution Detection.
Student project regarding out-of-domain text classification methods comparison on CLINC150 dataset.
Project Code for ICML UDL Workshop 2021 Submission
Code for "BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning"
TensorFlow 2 implementation of the paper Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data (https://arxiv.org/abs/2002.11297).
Source code for 《Energy-based Unknown Intent Detection with Data Manipulation》, which is accepted by Findings of ACL, 2021.
Baseline for out-of-distribution detection
Repository for UAI 2021 paper "Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fail at Reliable OOD Detection".
We propose a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection to various types of adversarial OOD inputs and establishes state-of-the-art performance.
Exploring the link between uncertainty estimates obtained via "exact" Bayesian inference and out-of-distribution (OOD) detection.
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.
Robust Out-of-distribution Detection in Neural Networks
A re-implementation project of Serra et al.: “Input complexity and out-of-distribution detection with likelihood-based generative models"
Post-hoc Out-of-Distribution Detection
[Under Progress] Code & Data for the AAAI 2020 Paper "Likelihood Ratios and Generative Classifiers For Unsupervised OOD Detection In Task-Based Dialog" - Varun Gangal, Abhinav Arora, Arash Einolghozati, Sonal Gupta
Add a description, image, and links to the ood-detection topic page so that developers can more easily learn about it.
To associate your repository with the ood-detection topic, visit your repo's landing page and select "manage topics."