{"total":97,"rows":[{"id":"2105.03075","title":"A Survey of Data Augmentation Approaches for NLP","created":"2021-05-07","cited_by_count":409,"area":null},{"id":"2106.04619","title":"Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style","created":"2021-06-08","cited_by_count":312,"area":null},{"id":"2105.11120","title":"A Fourier-based Framework for Domain Generalization","created":"2021-05-24","cited_by_count":290,"area":null},{"id":"2102.11273","title":"On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness","created":"2021-02-22","cited_by_count":97,"area":null},{"id":"2205.01491","title":"A Comprehensive Survey of Image Augmentation Techniques for Deep Learning","created":"2022-05-03","cited_by_count":97,"area":null},{"id":"2110.01852","title":"Data Augmentation Approaches in Natural Language Processing: A Survey","created":"2021-10-05","cited_by_count":82,"area":null},{"id":"2106.10800","title":"Lossy Compression for Lossless Prediction","created":"2021-06-21","cited_by_count":52,"area":null},{"id":"2201.01212","title":"AutoBalance: Optimized Loss Functions for Imbalanced Data","created":"2022-01-04","cited_by_count":49,"area":null},{"id":"2302.00236","title":"Generative Adversarial Symmetry Discovery","created":"2023-02-01","cited_by_count":42,"area":null},{"id":"2204.07596","title":"Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning","created":"2022-04-15","cited_by_count":40,"area":null},{"id":"2210.06441","title":"How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization","created":"2022-10-12","cited_by_count":40,"area":null},{"id":"2106.05586","title":"Data augmentation in Bayesian neural networks and the cold posterior effect","created":"2021-06-10","cited_by_count":36,"area":null},{"id":"2205.12141","title":"One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks","created":"2022-05-24","cited_by_count":29,"area":null},{"id":"2202.12230","title":"Sample Efficiency of Data Augmentation Consistency Regularization","created":"2022-02-24","cited_by_count":28,"area":null},{"id":"2110.02180","title":"Noisy Feature Mixup","created":"2021-10-05","cited_by_count":27,"area":null},{"id":"2305.17476","title":"Toward Understanding Generative Data Augmentation","created":"2023-05-27","cited_by_count":25,"area":null},{"id":"2210.05021","title":"The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective","created":"2022-10-10","cited_by_count":24,"area":null},{"id":"2302.11861","title":"Out-of-Domain Robustness via Targeted Augmentations","created":"2023-02-23","cited_by_count":24,"area":null},{"id":"2106.07512","title":"Last Layer Marginal Likelihood for Invariance Learning","created":"2021-06-14","cited_by_count":21,"area":null},{"id":"2202.07552","title":"A Theory of PAC Learnability under Transformation Invariances","created":"2022-02-15","cited_by_count":21,"area":null},{"id":"2306.00788","title":"Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression","created":"2023-06-01","cited_by_count":20,"area":null},{"id":"2303.08433","title":"The Benefits of Mixup for Feature Learning","created":"2023-03-15","cited_by_count":19,"area":null},{"id":"2203.01572","title":"Data Augmentation as Feature Manipulation","created":"2022-03-03","cited_by_count":18,"area":null},{"id":"2207.13249","title":"AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation","created":"2022-07-27","cited_by_count":18,"area":null},{"id":"2303.13458","title":"Optimization Dynamics of Equivariant and Augmented Neural Networks","created":"2023-03-23","cited_by_count":18,"area":null},{"id":"2403.03103","title":"Emergent Equivariance in Deep Ensembles","created":"2024-03-05","cited_by_count":18,"area":null},{"id":"2204.00887","title":"Dimensionless machine learning: Imposing exact units equivariance","created":"2022-04-02","cited_by_count":17,"area":null},{"id":"2206.09450","title":"Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting","created":"2022-06-19","cited_by_count":17,"area":null},{"id":"2202.12439","title":"Learning Invariant Weights in Neural Networks","created":"2022-02-25","cited_by_count":16,"area":null},{"id":"2103.14545","title":"DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization","created":"2021-03-26","cited_by_count":15,"area":null},{"id":"2210.13512","title":"Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup","created":"2022-10-24","cited_by_count":14,"area":null},{"id":"2205.13900","title":"How Tempering Fixes Data Augmentation in Bayesian Neural Networks","created":"2022-05-27","cited_by_count":13,"area":null},{"id":"2206.06544","title":"A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks","created":"2022-06-14","cited_by_count":12,"area":null},{"id":"2210.14019","title":"The Curious Case of Benign Memorization","created":"2022-10-25","cited_by_count":12,"area":null},{"id":"2310.14161","title":"Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation","created":"2023-10-22","cited_by_count":12,"area":null},{"id":"2406.13371","title":"Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment","created":"2024-06-19","cited_by_count":12,"area":null},{"id":"2410.12307","title":"DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain","created":"2024-10-16","cited_by_count":12,"area":null},{"id":"2502.05300","title":"Parameter Symmetry Potentially Unifies Deep Learning Theory","created":"2025-02-07","cited_by_count":12,"area":null},{"id":"2206.01909","title":"Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation","created":"2022-06-04","cited_by_count":11,"area":null},{"id":"2202.01832","title":"Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization","created":"2022-02-03","cited_by_count":8,"area":null},{"id":"2210.17467","title":"Iterative Teaching by Data Hallucination","created":"2022-10-31","cited_by_count":8,"area":null},{"id":"2410.01452","title":"Ensembles provably learn equivariance through data augmentation","created":"2024-10-02","cited_by_count":8,"area":null},{"id":"2104.04046","title":"Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review","created":"2021-04-08","cited_by_count":7,"area":null},{"id":"2106.07900","title":"ATD: Augmenting CP Tensor Decomposition by Self Supervision","created":"2021-06-15","cited_by_count":7,"area":null},{"id":"2205.06905","title":"Perspectives on Incorporating Expert Feedback into Model Updates","created":"2022-05-13","cited_by_count":7,"area":null},{"id":"2405.03730","title":"Tilt your Head: Activating the Hidden Spatial-Invariance of Classifiers","created":"2024-05-06","cited_by_count":7,"area":null},{"id":"2512.11855","title":"Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry","created":"2025-12-05","cited_by_count":7,"area":null},{"id":"2109.06926","title":"A trainable monogenic ConvNet layer robust in front of large contrast changes in image classification","created":"2021-09-14","cited_by_count":6,"area":null},{"id":"2310.16499","title":"Data Optimization in Deep Learning: A Survey","created":"2023-10-25","cited_by_count":6,"area":null},{"id":"2406.02883","title":"Nonlinear Transformations Against Unlearnable Datasets","created":"2024-06-05","cited_by_count":5,"area":null},{"id":"2508.08004","title":"Sample-aware RandAugment: Search-free Automatic Data Augmentation for Effective Image Recognition","created":"2025-08-11","cited_by_count":5,"area":null},{"id":"2206.10690","title":"Learning Continuous Rotation Canonicalization with Radial Beam Sampling","created":"2022-06-21","cited_by_count":4,"area":null},{"id":"2210.08363","title":"Data-Efficient Augmentation for Training Neural Networks","created":"2022-10-15","cited_by_count":4,"area":null},{"id":"2410.23461","title":"Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization","created":"2024-10-30","cited_by_count":4,"area":null},{"id":"2410.23672","title":"Provable Benefit of Cutout and CutMix for Feature Learning","created":"2024-10-31","cited_by_count":4,"area":null},{"id":"2606.24418","title":"Data Augmentation: A Fourier Analysis Perspective","created":"2026-06-23","cited_by_count":4,"area":null},{"id":"2204.10689","title":"Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition","created":"2022-04-22","cited_by_count":3,"area":null},{"id":"2204.11531","title":"VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization","created":"2022-04-25","cited_by_count":3,"area":null},{"id":"2305.13658","title":"Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflection","created":"2023-05-23","cited_by_count":3,"area":null},{"id":"2307.15475","title":"FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines","created":"2023-07-28","cited_by_count":3,"area":null},{"id":"2311.12800","title":"Understanding Data Augmentation from a Robustness Perspective","created":"2023-09-07","cited_by_count":3,"area":null},{"id":"2402.06855","title":"For Better or For Worse? Learning Minimum Variance Features With Label Augmentation","created":"2024-02-10","cited_by_count":3,"area":null},{"id":"2403.11792","title":"SETA: Semantic-Aware Token Augmentation for Domain Generalization","created":"2024-03-18","cited_by_count":3,"area":null},{"id":"2509.11218","title":"Geometrically Constrained and Token-Based Probabilistic Spatial Transformers","created":"2025-09-14","cited_by_count":3,"area":null},{"id":"2509.24886","title":"Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks","created":"2025-09-29","cited_by_count":3,"area":null},{"id":"2106.04114","title":"Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction","created":"2021-06-08","cited_by_count":2,"area":null},{"id":"2111.02034","title":"Building Legal Datasets","created":"2021-11-03","cited_by_count":2,"area":null},{"id":"2301.08606","title":"Data Augmentation for Modeling Human Personality: The Dexter Machine","created":"2023-01-20","cited_by_count":2,"area":null},{"id":"2304.02714","title":"Learning Stage-wise GANs for Whistle Extraction in Time-Frequency Spectrograms","created":"2023-04-05","cited_by_count":2,"area":null},{"id":"2304.06326","title":"Understanding Overfitting in Adversarial Training via Kernel Regression","created":"2023-04-13","cited_by_count":2,"area":null},{"id":"2309.16672","title":"Learning to Transform for Generalizable Instance-wise Invariance","created":"2023-09-28","cited_by_count":2,"area":null},{"id":"2405.19995","title":"Symmetries in Overparametrized Neural Networks: A Mean-Field View","created":"2024-05-30","cited_by_count":2,"area":null},{"id":"2510.03528","title":"Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance","created":"2025-10-03","cited_by_count":2,"area":null},{"id":"2110.07668","title":"Augmenting Imitation Experience via Equivariant Representations","created":"2021-10-14","cited_by_count":1,"area":null},{"id":"2110.08634","title":"Towards Robust Waveform-Based Acoustic Models","created":"2021-10-16","cited_by_count":1,"area":null},{"id":"2301.12289","title":"Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer","created":"2023-01-28","cited_by_count":1,"area":null},{"id":"2304.05895","title":"Towards Understanding How Data Augmentation Works with Imbalanced Data","created":"2023-04-12","cited_by_count":1,"area":null},{"id":"2408.14381","title":"Learning Tree-Structured Composition of Data Augmentation","created":"2024-08-26","cited_by_count":1,"area":null},{"id":"2505.21813","title":"Optimizing Data Augmentation through Bayesian Model Selection","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2507.11161","title":"How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction","created":"2025-07-15","cited_by_count":1,"area":null},{"id":"2510.08178","title":"Robust Canonicalization through Bootstrapped Data Re-Alignment","created":"2025-10-09","cited_by_count":1,"area":null},{"id":"2604.14498","title":"Improving Machine Learning Performance with Synthetic Augmentation","created":"2026-04-16","cited_by_count":1,"area":null},{"id":"2605.15306","title":"How Data Augmentation Shapes Neural Representations","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2605.31100","title":"Vector Linking via Cross-Model Local Isometric Consistency","created":"2026-05-29","cited_by_count":1,"area":null},{"id":"2609.08133","title":"Sparse Data Augmentation for Optimization with Provable Guarantees","created":"2026-09-08","cited_by_count":1,"area":null},{"id":"2105.05052","title":"Joint Text and Label Generation for Spoken Language Understanding","created":"2021-05-11","cited_by_count":0,"area":null},{"id":"2110.08430","title":"Metadata Shaping: Natural Language Annotations for the Tail","created":"2021-10-16","cited_by_count":0,"area":null},{"id":"2212.04059","title":"MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation","created":"2022-12-08","cited_by_count":0,"area":null},{"id":"2310.01739","title":"Randomized Dimension Reduction with Statistical Guarantees","created":"2023-10-03","cited_by_count":0,"area":null},{"id":"2312.04911","title":"Collinear datasets augmentation using Procrustes validation sets","created":"2023-12-08","cited_by_count":0,"area":null},{"id":"2409.15200","title":"Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast","created":"2024-09-23","cited_by_count":0,"area":null},{"id":"2512.14338","title":"Implicit Bias and Invariance: How Hopfield Networks Efficiently Learn Graph Orbits","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2601.19124","title":"Leveraging Sentence-oriented Augmentation and Transformer-Based Architecture for Vietnamese-Bahnaric Translation","created":"2026-01-27","cited_by_count":0,"area":null},{"id":"2602.06611","title":"Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2602.14423","title":"The geometry of invariant learning: an information-theoretic analysis of data augmentation and generalization","created":"2026-02-16","cited_by_count":0,"area":null},{"id":"2605.06931","title":"Target-Aware Data Augmentation for SAT Prediction","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2606.07382","title":"Covariance Shrinkage via Stochastic Interpolation","created":"2026-06-05","cited_by_count":0,"area":null}],"next_cursor":null}