I'm now interested in Learning to Adapt to Domain Shift
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Updated
Jul 19, 2023 - Python
I'm now interested in Learning to Adapt to Domain Shift
Project Page (ObjectDR)
Nearest Category Generalization
Implementation of the algorithms from "Learning Invariant Representations under General Interventions on the Response"
Out of Distribution Performance of State of Art Vision Model - Robustness evaluation
Prudent Response Surface Models combine predictions with confidence scores and uncertainty levels, allowing their use in downstream analysis even for high-uncertainty or out-of-distribution inputs.
Intuitive evaluation of out-of-distribution detectors using simple toy examples.
Official repository of STONE (KDD 2024)
Scripts, figures and working notes for the participation in FungiCLEF-2022, part of the 13th CLEF Conference, 2022
Replication package of the paper "On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Languages Models of Code" (FSE 2023)
[IV 2024] Official code for "Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection"
This work is a analysis of representations acquired for standard, OOD and Biased data on numerous objective functions.
Deep neural networks have garnered tremendous excitement in recent years thanks to their superior learning capacity in the presence of abundant data resources. However, collecting an exhaustive dataset covering all possible scenarios is often slow, expensive, and even impractical. The goal of this project is to devise a new learning framework th…
AAAI 2021. Neural Sequence-to-grid Module for Learning Symbolic Rules
Masking Strategies for Background Bias Removal in Computer Vision Models (ICCVW OODCV 2023 paper)
Repository for the paper "Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey"
The Limits of Fair Medical Imaging AI In The Wild
[NeurIPS 2023] "Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift" by Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He.
[NeurIPS 2023 (Spotlight)] Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
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