[IJCV 2022] Pytorch codes for Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
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Updated
Mar 20, 2022 - Python
[IJCV 2022] Pytorch codes for Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
PyTorch implementation of our CVPR 2024 paper "Unified Entropy Optimization for Open-Set Test-Time Adaptation"
Implementation of ICCV'23 paper "Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation"
Python code for detecting and learning new classes of threats present in crops
Source code of PRJ paper "Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds"
Code Implementation of "Unsupervised Recognition of Unknown Objects for Open-World Object Detection"
A toolbox for one-class classification and open set recognition based on intra-class splitting
Official PyTorch implementation of the paper “Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection”, open-set anomaly detection, few-shot anomaly detection, semi-supervised anomaly detection.
Machine learning project conducted together with Volvo Cars
1st Place Code for FungiCLEF 2023 Competition from UstcAIGroup
[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes. Dealing with out-of-distribution detection or open-set recognition in semantic segmentation.
Official implementation of "Extreme Value Meta-Learning for Few-Shot Open-Set Recognition of Hyperspectral Images" (TGRS'23)
"A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?" (CVPR 2024)
Fooling Machine Learning Models: A Novel Out-of-Distribution Attack through Generative Adversarial Networks
VLG: General Video Recognition with Web Textual Knowledge (https://arxiv.org/abs/2212.01638)
Implementation of CVPR'23 paper "Glocal Energy-based Learning for Few-Shot Open-Set Recognition"
This repository contains the code used to create the results presented in the paper: "From Coarse to Fine-Grained Open-Set Recognition". We investigate the role of label granularity, semantic similarity, and hierarchical representations in open-set recognition (OSR) with an OSR-benchmark based on iNat2021.
Official PyTorch implementation for "SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection"
Official code for paper "OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning"
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