What Paired Evaluations Reveal under Visual Perturbations
cs.CV, stat.AP
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/google-research/reassessed-imagenet
Terminology
Sources
- Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains
- Classification Confidence Estimation with Test-Time Data-Augmentation
- Are we done with ImageNet?
- Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
- Reproducible scaling laws for contrastive language-image learning
- Meta CLIP 2: A Worldwide Scaling Recipe
- Data Filtering Networks
- DataComp: In search of the next generation of multimodal datasets
- Selective Classification for Deep Neural Networks
- Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
- An Inverse Scaling Law for CLIP Training
- CLIPA-v2: Scaling CLIP Training with 81.1% Zero-shot ImageNet Accuracy within a \$10,000 Budget; An Extra \$4,000 Unlocks 81.8% Accuracy
- Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
- ImageNet Large Scale Visual Recognition Challenge
- VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models
- LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
- LAION-5B: An open large-scale dataset for training next generation image-text models
- EVA-CLIP: Improved Training Techniques for CLIP at Scale
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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