CIFAR-100

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Note

anchor is the dataset's official page, which is the most authoritative reference that exists for it: the underlying reference is Krizhevsky's 2009 technical report "Learning multiple layers of features from tiny images", which carries no arXiv id or DOI

Findings

• IC-028SPADE, an abstaining classifier built on top of ResNet, ViT, and VGG models, detects out-of-distribution and adversarial samples with provable guarantees. [train]
• IC-035Removing the inductive bias of locality from Vision Transformers improves or matches performance on classification and regression tasks. [eval]
• IC-036Removing locality from Vision Transformers improves performance in self-supervised learning via Masked Autoencoding. [eval]
• IC-038Few embedding dimensions drive the modality gap in CLIP and SigLIP [eval]
• IC-077VGG-16's loss landscape barrier height distinguishes adversarial from real inputs, enabling a detection method that outperforms baselines on DeepFool and C&W attacks [eval]
• IC-1017Vision and language models pre-trained on noisy data exhibit degraded OOD transfer that is partially recoverable via SVD-based feature-space regularization [eval]
• IC-1105PAC-Bayes generalization bounds for discrete class prompts on CLIP are within a few percentage points of the actual test error across CIFAR-10, CIFAR-100, ImageNet, FMOW, and OfficeHome [eval]
• IC-1106CLIP prompts found by greedy search do not fit random labels: train and test error drop in tandem as the fraction of flipped labels increases, unlike a linear probe which achieves near-random accuracy [eval]
• IC-137Pre-trained ResNet34 and ViT-B features on CIFAR-100 exhibit a block-diagonal class-correlation structure, with ViT-B showing higher intra-class correlation (0.35) than ResNet34 (0.25) [eval]
• IC-1544The latent spaces of pretrained foundational models across vision and text are not related by a single class of geometric transformations; the optimal alignment depends on the specific model pair, architecture, and dataset. [eval]
• IC-295The ViT model's ECE can be reduced to near-zero by trivial mean-replacement recalibration while maintaining test accuracy, but NLL increases from 65.35 to 144.66, demonstrating that ECE and accuracy alone are an insufficient reporting standard for calibration [eval]
• IC-318CLIP backbones from different architectures (ViTs and ResNets) trained with the same data and objective exhibit complementary strengths, with an oracle per-image backbone selection improving zero-shot accuracy by up to 43.5% over the best single backbone [eval]
• IC-448CLIP ViT-L/14 achieves 0% accuracy under 2/255 and 4/255 L-infinity adversarial perturbations across all 15 evaluation datasets [eval]
• IC-737CLIP ViT-B/16 binarized dot products yield 0.50–0.58 accuracy on binary concept presence queries across five image classification datasets [eval]
• IC-738BLIP-2 ViT-G FlanT5XL achieves 0.70–0.87 zero-shot accuracy on binary concept presence queries, competitive on most datasets but weaker on fine-grained CUB-200 [eval]
• IC-739GPT-3.5-turbo-0613 combined with CLIP produces more faithful concept-salience pseudo-labels than LLaMA-2-13B-Chat, InstructBLIP, or LLaVA-1.5B on most of five datasets [eval]
• IC-990ResNet-50 and DenseNet-101 exhibit a higher mean-to-variance ratio in penultimate pre-ReLU activations for in-distribution samples than for out-of-distribution samples, and the activation-based scaling factor is well-separated between ID and OOD [eval]