[NeurIPS 2023] RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
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Updated
Feb 27, 2024 - Python
[NeurIPS 2023] RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
[ICCV 2021 Oral] Deep Evidential Action Recognition
👽 Out-of-Distribution Detection with PyTorch
[ICCV'23] Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Feature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)
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.
ICCV 2023: CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No
A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.
Robust Out-of-distribution Detection in Neural Networks
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.
[Findings of EMNLP 2022] Holistic Sentence Embeddings for Better Out-of-Distribution Detection
A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.
Official PyTorch implementation of MOOD series: (1) MOODv1: Rethinking Out-of-distributionDetection: Masked Image Modeling Is All You Need. (2) MOODv2: Masked Image Modeling for Out-of-Distribution Detection.
[ICCV'23 Oral] Unmasking Anomalies in Road-Scene Segmentation
Official PyTorch implementation of "Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data" (NeurIPS'23)
Source code for 《Energy-based Unknown Intent Detection with Data Manipulation》, which is accepted by Findings of ACL, 2021.
[WACV 2024] Official PyTorch implementation of Brainomaly
Code for "BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning"
A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
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