OOD Generalization and Detection (ACL 2020)
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Updated
Apr 15, 2020 - Python
OOD Generalization and Detection (ACL 2020)
AAAI 2021. Neural Sequence-to-grid Module for Learning Symbolic Rules
ImageNet-R(endition) and DeepAugment (ICCV 2021)
Implementation and Benchmark Splits to study Out-of-Distribution Generalization in Deep Metric Learning.
Code for the ICLR 2021 Paper "In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness"
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.
AICONSlab's DL benchmarking platform to OOD data in MRI
[ICLR 2022] Official pytorch implementation of "Uncertainty Modeling for Out-of-Distribution Generalization" in International Conference on Learning Representations (ICLR) 2022.
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.
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.
This work is a analysis of representations acquired for standard, OOD and Biased data on numerous objective functions.
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.
Size-Invariant Graph Representations for Graph Classification Extrapolations (ICML 2021 Long Talk)
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…
Nearest Category Generalization
Out of Distribution Performance of State of Art Vision Model - Robustness evaluation
Official PyTorch implementation of Fully Attentional Networks
[MICCAI 2022 Best Paper Finalist] Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation
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