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Found 6 papers, 6 papers with code
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
