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Found 9 papers, 6 papers with code
In Search of Forgotten Domain Generalization
In the ImageNet era of computer vision, evaluation sets for measuring a model's OOD performance were designed to be strictly OOD with respect to style.
Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach
In an era where personalized technology is increasingly intertwined with daily life, traditional eye-tracking systems and autofocal glasses face a significant challenge: the need for frequent, user-specific calibration, which impedes their practicality.
InfoNCE: Identifying the Gap Between Theory and Practice
Hence, a more realistic assumption is that all latent factors change, with a continuum of variability across these factors.
Effective pruning of web-scale datasets based on complexity of concept clusters
Using a simple and intuitive complexity measure, we are able to reduce the training cost to a quarter of regular training.
Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?
Foundation models like CLIP are trained on hundreds of millions of samples and effortlessly generalize to new tasks and inputs.
If your data distribution shifts, use self-learning
We demonstrate that self-learning techniques like entropy minimization and pseudo-labeling are simple and effective at improving performance of a deployed computer vision model under systematic domain shifts.
Ranked #1 on
Unsupervised Domain Adaptation
on ImageNet-A
(using extra training data)
Improving robustness against common corruptions by covariate shift adaptation
With the more robust DeepAugment+AugMix model, we improve the state of the art achieved by a ResNet50 model up to date from 53. 6% mCE to 45. 4% mCE.
Ranked #4 on
Unsupervised Domain Adaptation
on ImageNet-R
A simple way to make neural networks robust against diverse image corruptions
The human visual system is remarkably robust against a wide range of naturally occurring variations and corruptions like rain or snow.
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
Ranked #1 on
Robust Object Detection
on MS COCO
