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← Learning Types
Deep Learning
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Learning Types
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Distribution Shift
11 papers
Papers per year
2021: 2
2
2022: 3
3
2023: 1
1
2024: 4
4
2026: 1
1
Papers
The LUMirage: An independent evaluation of zero-shot performance in the LUMIR challenge
MIDL 2026
Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox
NIPS 2024
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning
AAAI 2024
MaxEnt Loss: Calibrating Graph Neural Networks under Out-of-Distribution Shift (Student Abstract)
AAAI 2024
Distance-aware Calibration for Pre-trained Language Models
EMNLP 2024
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift
NIPS 2023
Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift
NIPS 2022
OoDHDR-Codec: Out-of-Distribution Generalization for HDR Image Compression
AAAI 2022
On the Impact of Spurious Correlation for Out-of-Distribution Detection
AAAI 2022
Introspective Distillation for Robust Question Answering
NIPS 2021
Deep Stable Learning for Out-of-Distribution Generalization
CVPR 2021
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