Swin Transformer reaches 87.3% ImageNet accuracy and sets new records on COCO detection and ADE20K segmentation by replacing global self-attention with shifted-window local attention inside a hierarchical pyramid.
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mixup: Beyond Empirical Risk Minimization
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abstract
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures. We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.
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- abstract Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels. By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neur
co-cited works
representative citing papers
TCP-SSM conditions stable poles on visual tokens to explicitly control memory decay and oscillation in SSMs, cutting computation up to 44% while matching or exceeding accuracy on classification, segmentation, and detection.
An efficiently computable HS-Jacobian acts as a conservative mapping for projections onto polyhedral sets, supporting provably convergent Adam-based end-to-end training of linearly constrained deep neural networks.
LG-CoTrain, an LLM-guided co-training method, outperforms classical semi-supervised baselines for crisis tweet classification in low-resource settings with 5-25 labeled examples per class.
LookWhen factorizes video recognition into learning when, where, and what to compute via uniqueness-based token selection and dual-teacher distillation, achieving better accuracy-FLOPs trade-offs than baselines on multiple datasets.
PARSE improves domain generalization accuracy by factoring recognition into visual primitives and their spatial relational compositions learned end-to-end with differentiable predicates.
LEGO uses multiple generator-specific LoRA modules modulated by an MLP and fused with attention to detect synthetic images, achieving better performance than prior methods while using under 10% of the training data.
SignMAE uses segmentation-driven masking in a mask-and-reconstruct self-supervised task to learn fine-grained sign representations, achieving state-of-the-art accuracy on WLASL, NMFs-CSL, and Slovo with fewer frames and modalities.
A replay method for continual face forgery detection condenses real-fake distribution discrepancies into compact maps and synthesizes compatible samples from current real faces to reduce forgetting under tight memory budgets without storing historical images.
Machine unlearning conflates reversing the influence of specific training examples (untraining) with removing the full underlying distribution or behavior (unlearning).
DREAM introduces Masking Warmup and Semantically Aligned Decoding to let a single encoder handle both contrastive alignment and masked generation, yielding gains over CLIP and FLUID on understanding and generation benchmarks.
ST-BCP tightens the coverage bound in Backward Conformal Prediction by applying a computable data-dependent transformation to nonconformity scores, reducing the average gap from 4.20% to 1.12% on benchmarks while proving superiority over the identity baseline.
Chronos pretrains transformer models on tokenized time series to deliver strong zero-shot forecasting across diverse domains.
The DFDC dataset is the largest public collection of face-swapped videos and supports detectors that generalize to in-the-wild deepfakes.
GAMR introduces geometric-aware manifold regularization via virtual outlier synthesis to enhance intra-class compactness and inter-class separation, improving robustness to noisy labels beyond passive sample filtering.
HamBR uses Spherical HMC to probe ambiguous regions and synthesize virtual outliers with energy-based repulsion to restore decision boundaries degraded by noisy labels, achieving SOTA on CIFAR and real-world benchmarks.
LiBaGS scores and selects synthetic data near decision boundaries using proximity, uncertainty, density, and validity, with boundary-gap allocation and marginal stopping to improve training accuracy.
CORF unifies domain generalization and class-incremental learning via selective sample refinement with spatial maps and confidence weighting plus cascaded relational distillation.
CircleID introduces a controlled dataset of 46,155 circles from 66 writers and 8 pens, with competition results showing top accuracies of 64.8% for open-set writer identification and 92.7% for pen classification.
Mixing real UAV imagery with 2101 AI-generated image-mask pairs improves semantic segmentation F1 scores for fine-grained forest species by over 15 percentage points overall and up to 30 points for rare classes.
CHCL aligns a Cheeger-Hodge joint signature across graph augmentations to produce embeddings that remain stable under local structural changes.
TranCLR models continuous skeleton action spaces with transitional anchors and multi-level manifold calibration, yielding smoother and more accurate representations than binary contrastive methods.
PAC-Bayes bounds for Gibbs posteriors are obtained via singular learning theory, producing explicit and tighter posterior-averaged risk bounds that adapt to data structure in overparameterized models.
HG-DTGL integrates human gaze as an extra teacher in mean-teacher learning via GazeMix, MGP module and Gaze Loss, reporting superior segmentation across ten organs on multiple modalities.
citing papers explorer
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TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles
TCP-SSM conditions stable poles on visual tokens to explicitly control memory decay and oscillation in SSMs, cutting computation up to 44% while matching or exceeding accuracy on classification, segmentation, and detection.
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Efficient and provably convergent end-to-end training of deep neural networks with linear constraints
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LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
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LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute
LookWhen factorizes video recognition into learning when, where, and what to compute via uniqueness-based token selection and dual-teacher distillation, achieving better accuracy-FLOPs trade-offs than baselines on multiple datasets.
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Domain Generalization through Spatial Relation Induction over Visual Primitives
PARSE improves domain generalization accuracy by factoring recognition into visual primitives and their spatial relational compositions learned end-to-end with differentiable predicates.
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LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection
LEGO uses multiple generator-specific LoRA modules modulated by an MLP and fused with attention to detect synthetic images, achieving better performance than prior methods while using under 10% of the training data.
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SignMAE: Segmentation-Driven Self-Supervised Learning for Sign Language Recognition
SignMAE uses segmentation-driven masking in a mask-and-reconstruct self-supervised task to learn fine-grained sign representations, achieving state-of-the-art accuracy on WLASL, NMFs-CSL, and Slovo with fewer frames and modalities.
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Direct Discrepancy Replay: Distribution-Discrepancy Condensation and Manifold-Consistent Replay for Continual Face Forgery Detection
A replay method for continual face forgery detection condenses real-fake distribution discrepancies into compact maps and synthesizes compatible samples from current real faces to reduce forgetting under tight memory budgets without storing historical images.
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Is your algorithm unlearning or untraining?
Machine unlearning conflates reversing the influence of specific training examples (untraining) with removing the full underlying distribution or behavior (unlearning).
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Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation
DREAM introduces Masking Warmup and Semantically Aligned Decoding to let a single encoder handle both contrastive alignment and masked generation, yielding gains over CLIP and FLUID on understanding and generation benchmarks.
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ST-BCP: Tightening Coverage Bound for Backward Conformal Prediction via Non-Conformity Score Transformation
ST-BCP tightens the coverage bound in Backward Conformal Prediction by applying a computable data-dependent transformation to nonconformity scores, reducing the average gap from 4.20% to 1.12% on benchmarks while proving superiority over the identity baseline.
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Chronos: Learning the Language of Time Series
Chronos pretrains transformer models on tokenized time series to deliver strong zero-shot forecasting across diverse domains.
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The DeepFake Detection Challenge (DFDC) Dataset
The DFDC dataset is the largest public collection of face-swapped videos and supports detectors that generalize to in-the-wild deepfakes.
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GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels
GAMR introduces geometric-aware manifold regularization via virtual outlier synthesis to enhance intra-class compactness and inter-class separation, improving robustness to noisy labels beyond passive sample filtering.
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HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels
HamBR uses Spherical HMC to probe ambiguous regions and synthesize virtual outliers with energy-based repulsion to restore decision boundaries degraded by noisy labels, achieving SOTA on CIFAR and real-world benchmarks.
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LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection
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Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning
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ICDAR 2026 Competition on Writer Identification and Pen Classification from Hand-Drawn Circles
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Cheeger--Hodge Contrastive Learning for Structurally Robust Graph Representation Learning
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Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchors
TranCLR models continuous skeleton action spaces with transitional anchors and multi-level manifold calibration, yielding smoother and more accurate representations than binary contrastive methods.
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PAC-Bayes Bounds for Gibbs Posteriors via Singular Learning Theory
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Human Gaze-based Dual Teacher Guidance Learning for Semi-Supervised Medical Image Segmentation
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LoFT: Parameter-Efficient Fine-Tuning for Long-tailed Semi-Supervised Learning in Open-World Scenarios
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Normalized Matching Transformer
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YOLOv12: Attention-Centric Real-Time Object Detectors
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SpikeDet: Better Firing Patterns for Accurate and Energy-Efficient Object Detection with Spiking Neural Networks
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Revisiting Feature Prediction for Learning Visual Representations from Video
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Sharpness-Aware Minimization for Efficiently Improving Generalization
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Learning Data Augmentation Strategies for Object Detection
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Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label
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Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
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CAST: Channel-Aware Spatial Transfer Learning with Pseudo-Image Radar for Sign Language Recognition
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Agentic AIs Are the Missing Paradigm for Out-of-Distribution Generalization in Foundation Models
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HiMix: Hierarchical Artifact-aware Mixup for Generalized Synthetic Image Detection
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Investigating Bias and Fairness in Appearance-based Gaze Estimation
First large-scale fairness audit of gaze estimators reveals sizable accuracy disparities by ethnicity and gender, with existing mitigation methods providing only marginal fairness gains.
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Beyond Surface Artifacts: Capturing Shared Latent Forgery Knowledge Across Modalities
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Multi-Aspect Knowledge Distillation for Language Model with Low-rank Factorization
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Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
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Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels
ORDAC adaptively corrects noisy ordinal labels via dynamic label distribution adjustments, yielding lower error and higher recall on noisy Adience and Diabetic Retinopathy benchmarks.
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Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection
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