{"total":91,"rows":[{"id":"2107.13586","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","created":"2021-07-28","cited_by_count":2234,"area":null},{"id":"2105.03075","title":"A Survey of Data Augmentation Approaches for NLP","created":"2021-05-07","cited_by_count":409,"area":null},{"id":"2102.08259","title":"Dataset Condensation with Differentiable Siamese Augmentation","created":"2021-02-16","cited_by_count":291,"area":null},{"id":"2106.15147","title":"SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption","created":"2021-06-29","cited_by_count":156,"area":null},{"id":"2104.07876","title":"Deep Stable Learning for Out-Of-Distribution Generalization","created":"2021-04-16","cited_by_count":153,"area":null},{"id":"2112.06409","title":"Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective","created":"2021-12-13","cited_by_count":116,"area":null},{"id":"2107.03158","title":"A Survey on Data Augmentation for Text Classification","created":"2021-07-07","cited_by_count":111,"area":null},{"id":"2103.12579","title":"MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition","created":"2021-03-23","cited_by_count":99,"area":null},{"id":"2103.16440","title":"Neural Transformation Learning for Deep Anomaly Detection Beyond Images","created":"2021-03-30","cited_by_count":99,"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":"2104.14551","title":"Ensembling with Deep Generative Views","created":"2021-04-29","cited_by_count":53,"area":null},{"id":"2204.13423","title":"Hybrid Relation Guided Set Matching for Few-shot Action Recognition","created":"2022-04-28","cited_by_count":50,"area":null},{"id":"2111.06399","title":"Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification","created":"2021-11-10","cited_by_count":35,"area":null},{"id":"2403.10075","title":"A survey of synthetic data augmentation methods in computer vision","created":"2024-03-15","cited_by_count":34,"area":null},{"id":"2110.10232","title":"Test time Adaptation through Perturbation Robustness","created":"2021-10-19","cited_by_count":28,"area":null},{"id":"2112.07928","title":"Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification","created":"2021-12-15","cited_by_count":24,"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":"2103.17220","title":"Scale-aware Automatic Augmentation for Object Detection","created":"2021-03-31","cited_by_count":23,"area":null},{"id":"2105.08149","title":"Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments","created":"2021-05-17","cited_by_count":23,"area":null},{"id":"2103.00429","title":"Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models","created":"2021-02-28","cited_by_count":22,"area":null},{"id":"2106.16245","title":"How to Train Your MAML to Excel in Few-Shot Classification","created":"2021-06-30","cited_by_count":22,"area":null},{"id":"2206.09979","title":"Mitigating Data Heterogeneity in Federated Learning with Data Augmentation","created":"2022-06-20","cited_by_count":21,"area":null},{"id":"2202.13248","title":"Automated Data Augmentations for Graph Classification","created":"2022-02-26","cited_by_count":20,"area":null},{"id":"2108.02888","title":"Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach","created":"2021-08-05","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":"2212.14453","title":"Learning Multimodal Data Augmentation in Feature Space","created":"2022-12-29","cited_by_count":18,"area":null},{"id":"2107.11186","title":"LARGE: Latent-Based Regression through GAN Semantics","created":"2021-07-22","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":"2206.07736","title":"Improving Diversity with Adversarially Learned Transformations for Domain Generalization","created":"2022-06-15","cited_by_count":16,"area":null},{"id":"2111.08190","title":"Learning Augmentation Distributions using Transformed Risk Minimization","created":"2021-11-16","cited_by_count":13,"area":null},{"id":"2212.04873","title":"Multimodal Prototype-Enhanced Network for Few-Shot Action Recognition","created":"2022-12-09","cited_by_count":13,"area":null},{"id":"2401.01764","title":"Understanding the Detrimental Class-level Effects of Data Augmentation","created":"2023-12-07","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":"2301.03330","title":"HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition","created":"2023-01-09","cited_by_count":12,"area":null},{"id":"2305.16663","title":"GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks","created":"2023-05-26","cited_by_count":10,"area":null},{"id":"2210.03773","title":"In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?","created":"2022-10-07","cited_by_count":9,"area":null},{"id":"2102.08310","title":"Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation","created":"2021-02-16","cited_by_count":8,"area":null},{"id":"2212.10553","title":"RangeAugment: Efficient Online Augmentation with Range Learning","created":"2022-12-20","cited_by_count":8,"area":null},{"id":"2305.16379","title":"Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning","created":"2023-05-25","cited_by_count":8,"area":null},{"id":"2106.03374","title":"RegMix: Data Mixing Augmentation for Regression","created":"2021-06-07","cited_by_count":7,"area":null},{"id":"2210.13428","title":"PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds","created":"2022-10-24","cited_by_count":7,"area":null},{"id":"2304.00022","title":"What Makes for Effective Few-shot Point Cloud Classification?","created":"2023-03-31","cited_by_count":7,"area":null},{"id":"2311.06218","title":"Semantic-aware Video Representation for Few-shot Action Recognition","created":"2023-11-10","cited_by_count":7,"area":null},{"id":"2108.07958","title":"Semantic Perturbations with Normalizing Flows for Improved Generalization","created":"2021-08-18","cited_by_count":6,"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":"2501.01441","title":"Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems","created":"2024-12-26","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":"2202.06590","title":"A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images","created":"2022-02-14","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":"2312.01083","title":"Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CP$\\mathbf{M^2}$C)","created":"2023-12-02","cited_by_count":4,"area":null},{"id":"2407.05657","title":"DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition","created":"2024-07-08","cited_by_count":4,"area":null},{"id":"2602.18043","title":"Spatio-temporal Decoupled Knowledge Compensator for Few-Shot Action Recognition","created":"2026-02-20","cited_by_count":4,"area":null},{"id":"2106.06657","title":"Provable Adaptation across Multiway Domains via Representation Learning","created":"2021-06-12","cited_by_count":3,"area":null},{"id":"2205.12606","title":"ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation","created":"2022-05-25","cited_by_count":3,"area":null},{"id":"2309.07578","title":"Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning","created":"2023-09-14","cited_by_count":3,"area":"offlinerl"},{"id":"2501.14122","title":"Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters","created":"2025-01-23","cited_by_count":3,"area":"deeprl"},{"id":"2101.08106","title":"Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation","created":"2021-01-20","cited_by_count":2,"area":"distill"},{"id":"2210.10879","title":"G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR","created":"2022-10-19","cited_by_count":2,"area":null},{"id":"2302.01806","title":"Mitigating Data Scarcity for Large Language Models","created":"2023-02-03","cited_by_count":2,"area":null},{"id":"2307.01985","title":"Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition","created":"2023-07-05","cited_by_count":2,"area":null},{"id":"2307.11375","title":"LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space","created":"2023-07-21","cited_by_count":2,"area":null},{"id":"2401.08345","title":"Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-$\\mathrm{M^2}$DF)","created":"2024-01-16","cited_by_count":2,"area":null},{"id":"2402.02275","title":"SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach","created":"2024-02-03","cited_by_count":2,"area":null},{"id":"2403.15010","title":"Clean-image Backdoor Attacks","created":"2024-03-22","cited_by_count":2,"area":null},{"id":"2106.08693","title":"ParticleAugment: Sampling-Based Data Augmentation","created":"2021-06-16","cited_by_count":1,"area":null},{"id":"2109.01691","title":"ALLWAS: Active Learning on Language models in WASserstein space","created":"2021-09-03","cited_by_count":1,"area":null},{"id":"2110.07668","title":"Augmenting Imitation Experience via Equivariant Representations","created":"2021-10-14","cited_by_count":1,"area":null},{"id":"2208.02011","title":"Equivariant Disentangled Transformation for Domain Generalization under Combination Shift","created":"2022-08-03","cited_by_count":1,"area":null},{"id":"2211.07959","title":"The Lean Data Scientist: Recent Advances towards Overcoming the Data Bottleneck","created":"2022-11-15","cited_by_count":1,"area":null},{"id":"2303.12499","title":"On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics","created":"2023-03-22","cited_by_count":1,"area":null},{"id":"2305.19889","title":"Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits","created":"2023-05-31","cited_by_count":1,"area":null},{"id":"2307.11808","title":"Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images","created":"2023-07-21","cited_by_count":1,"area":null},{"id":"2402.13465","title":"Unsupervised learning based object detection using Contrastive Learning","created":"2024-02-21","cited_by_count":1,"area":null},{"id":"2407.04325","title":"Understanding the Role of Invariance in Transfer Learning","created":"2024-07-05","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":"2410.13318","title":"Computational Approaches to Arabic-English Code-Switching","created":"2024-10-17","cited_by_count":1,"area":null},{"id":"2104.02653","title":"On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study","created":"2021-04-02","cited_by_count":0,"area":null},{"id":"2104.03843","title":"InAugment: Improving Classifiers via Internal Augmentation","created":"2021-04-08","cited_by_count":0,"area":null},{"id":"2105.09683","title":"DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images","created":"2021-05-20","cited_by_count":0,"area":null},{"id":"2111.03861","title":"What augmentations are sensitive to hyper-parameters and why?","created":"2021-11-06","cited_by_count":0,"area":null},{"id":"2207.08817","title":"Research Trends and Applications of Data Augmentation Algorithms","created":"2022-07-18","cited_by_count":0,"area":null},{"id":"2207.10271","title":"DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta","created":"2022-07-21","cited_by_count":0,"area":null},{"id":"2306.10959","title":"RaViTT: Random Vision Transformer Tokens","created":"2023-06-19","cited_by_count":0,"area":null},{"id":"2306.13050","title":"Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization","created":"2023-06-22","cited_by_count":0,"area":null},{"id":"2310.02885","title":"Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data","created":"2023-10-04","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":"2405.06383","title":"How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models?","created":"2024-05-10","cited_by_count":0,"area":null},{"id":"2405.07116","title":"CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning","created":"2024-05-12","cited_by_count":0,"area":null},{"id":"2501.14694","title":"Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection","created":"2025-01-24","cited_by_count":0,"area":null},{"id":"2502.15245","title":"Steganographic Embeddings as an Effective Data Augmentation","created":"2025-02-21","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}