{"total":92,"rows":[{"id":"2104.06644","title":"Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little","created":"2021-04-14","cited_by_count":177,"area":null},{"id":"2112.05135","title":"PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures","created":"2021-12-09","cited_by_count":131,"area":null},{"id":"2112.10740","title":"Are Large-scale Datasets Necessary for Self-Supervised Pre-training?","created":"2021-12-20","cited_by_count":117,"area":null},{"id":"2106.05963","title":"Learning to See by Looking at Noise","created":"2021-06-10","cited_by_count":92,"area":null},{"id":"2206.11488","title":"On the Importance and Applicability of Pre-Training for Federated Learning","created":"2022-06-23","cited_by_count":71,"area":null},{"id":"2211.11567","title":"Neural networks trained with SGD learn distributions of increasing complexity","created":"2022-11-21","cited_by_count":50,"area":null},{"id":"2311.15153","title":"Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture","created":"2023-11-26","cited_by_count":46,"area":null},{"id":"2106.04180","title":"Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models","created":"2021-06-08","cited_by_count":41,"area":null},{"id":"2401.01862","title":"A Vision Check-up for Language Models","created":"2024-01-03","cited_by_count":36,"area":null},{"id":"2103.13023","title":"Can Vision Transformers Learn without Natural Images?","created":"2021-03-24","cited_by_count":32,"area":null},{"id":"2401.14953","title":"Learning Universal Predictors","created":"2024-01-26","cited_by_count":31,"area":null},{"id":"2304.03626","title":"Asynchronous Federated Continual Learning","created":"2023-04-07","cited_by_count":29,"area":null},{"id":"2211.16412","title":"Procedural Image Programs for Representation Learning","created":"2022-11-29","cited_by_count":28,"area":null},{"id":"2110.03091","title":"Improving Fractal Pre-training","created":"2021-10-06","cited_by_count":26,"area":null},{"id":"2307.12532","title":"On the Connection between Pre-training Data Diversity and Fine-tuning Robustness","created":"2023-07-24","cited_by_count":25,"area":null},{"id":"2104.15023","title":"Post-training deep neural network pruning via layer-wise calibration","created":"2021-04-30","cited_by_count":24,"area":null},{"id":"2206.09132","title":"Pre-training Vision Transformers with Formula-driven Supervised Learning","created":"2022-06-18","cited_by_count":21,"area":null},{"id":"2203.15331","title":"CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters","created":"2022-03-29","cited_by_count":20,"area":null},{"id":"2303.01112","title":"Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves","created":"2023-03-02","cited_by_count":20,"area":null},{"id":"2210.07450","title":"ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation","created":"2022-10-14","cited_by_count":19,"area":null},{"id":"2306.06076","title":"Differentially Private Image Classification by Learning Priors from Random Processes","created":"2023-06-08","cited_by_count":19,"area":null},{"id":"2306.08842","title":"ViP: A Differentially Private Foundation Model for Computer Vision","created":"2023-06-15","cited_by_count":17,"area":null},{"id":"2207.05025","title":"PSP-HDRI$+$: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models","created":"2022-07-11","cited_by_count":15,"area":null},{"id":"2603.10055","title":"Training Language Models via Neural Cellular Automata","created":"2026-03-09","cited_by_count":15,"area":null},{"id":"2310.04780","title":"IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers","created":"2023-10-07","cited_by_count":14,"area":null},{"id":"2403.07514","title":"Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation","created":"2024-03-12","cited_by_count":14,"area":null},{"id":"2408.00677","title":"Scaling Backwards: Minimal Synthetic Pre-training?","created":"2024-08-01","cited_by_count":14,"area":null},{"id":"2307.14710","title":"Pre-training Vision Transformers with Very Limited Synthesized Images","created":"2023-07-27","cited_by_count":12,"area":null},{"id":"2212.10173","title":"On the Role of Parallel Data in Cross-lingual Transfer Learning","created":"2022-12-20","cited_by_count":11,"area":null},{"id":"2205.01676","title":"FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading","created":"2022-05-02","cited_by_count":9,"area":null},{"id":"2207.00052","title":"Visual Pre-training for Navigation: What Can We Learn from Noise?","created":"2022-06-30","cited_by_count":8,"area":null},{"id":"2309.17083","title":"SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning","created":"2023-09-29","cited_by_count":8,"area":null},{"id":"2511.13945","title":"Can You Learn to See Without Images? Procedural Warm-Up for Vision Transformers","created":"2025-11-17","cited_by_count":8,"area":null},{"id":"2112.00725","title":"The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single Image","created":"2021-12-01","cited_by_count":7,"area":null},{"id":"2210.10913","title":"Palm up: Playing in the Latent Manifold for Unsupervised Pretraining","created":"2022-10-19","cited_by_count":7,"area":null},{"id":"2201.00392","title":"Fast and High-Quality Image Denoising via Malleable Convolutions","created":"2022-01-02","cited_by_count":6,"area":null},{"id":"2204.04950","title":"Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data","created":"2022-04-11","cited_by_count":6,"area":null},{"id":"2204.07673","title":"Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations","created":"2022-04-15","cited_by_count":6,"area":null},{"id":"2206.04242","title":"OOD Augmentation May Be at Odds with Open-Set Recognition","created":"2022-06-09","cited_by_count":6,"area":null},{"id":"2401.03665","title":"Primitive Geometry Segment Pre-training for 3D Medical Image Segmentation","created":"2024-01-08","cited_by_count":6,"area":null},{"id":"2204.08226","title":"Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey","created":"2022-04-18","cited_by_count":5,"area":null},{"id":"2209.02397","title":"A Scene-Text Synthesis Engine Achieved Through Learning from Decomposed Real-World Data","created":"2022-09-06","cited_by_count":5,"area":null},{"id":"2305.17520","title":"USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution","created":"2023-05-27","cited_by_count":4,"area":null},{"id":"2311.06224","title":"Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization","created":"2023-11-10","cited_by_count":4,"area":null},{"id":"2405.09334","title":"Content-Based Image Retrieval for Multi-Class Volumetric Radiology Images: A Benchmark Study","created":"2024-05-15","cited_by_count":4,"area":null},{"id":"2511.10282","title":"Torch-Uncertainty: A Deep Learning Framework for Uncertainty Quantification","created":"2025-11-13","cited_by_count":4,"area":null},{"id":"2306.10798","title":"ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers","created":"2023-06-19","cited_by_count":3,"area":null},{"id":"2409.13535","title":"Formula-Supervised Visual-Geometric Pre-training","created":"2024-09-20","cited_by_count":3,"area":null},{"id":"2410.00511","title":"Pre-training with Synthetic Patterns for Audio","created":"2024-10-01","cited_by_count":3,"area":null},{"id":"2411.17376","title":"RealTraj: Towards Real-World Pedestrian Trajectory Forecasting","created":"2024-11-26","cited_by_count":3,"area":null},{"id":"2602.05184","title":"Towards Worst-Case Guarantees with Scale-Aware Interpretability","created":"2026-02-05","cited_by_count":3,"area":null},{"id":"2607.11064","title":"WiFi-JEPA: Self-supervised Learning for WiFi-CSI 3D Human Pose Estimation","created":"2026-07-13","cited_by_count":3,"area":null},{"id":"2302.08272","title":"Revisiting Hidden Representations in Transfer Learning for Medical Imaging","created":"2023-02-16","cited_by_count":2,"area":null},{"id":"2303.12722","title":"Learning Fractals by Gradient Descent","created":"2023-03-14","cited_by_count":2,"area":null},{"id":"2404.17931","title":"Critical Review for One-class Classification: recent advances and the reality behind them","created":"2024-04-27","cited_by_count":2,"area":null},{"id":"2409.06665","title":"Data Collection-free Masked Video Modeling","created":"2024-09-10","cited_by_count":2,"area":null},{"id":"2112.00113","title":"Beyond Flatland: Pre-training with a Strong 3D Inductive Bias","created":"2021-11-30","cited_by_count":1,"area":null},{"id":"2201.08465","title":"An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters","created":"2022-01-20","cited_by_count":1,"area":null},{"id":"2306.04955","title":"Degraded Polygons Raise Fundamental Questions of Neural Network Perception","created":"2023-06-08","cited_by_count":1,"area":null},{"id":"2311.04016","title":"Exploring Dataset-Scale Indicators of Data Quality","created":"2023-11-07","cited_by_count":1,"area":null},{"id":"2311.17232","title":"ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development","created":"2023-11-28","cited_by_count":1,"area":null},{"id":"2403.04353","title":"Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification","created":"2024-03-07","cited_by_count":1,"area":null},{"id":"2406.00298","title":"Complex Style Image Transformations for Domain Generalization in Medical Images","created":"2024-06-01","cited_by_count":1,"area":null},{"id":"2406.05923","title":"Contrastive Learning from Synthetic Audio Doppelg\\\"angers","created":"2024-06-09","cited_by_count":1,"area":null},{"id":"2408.05435","title":"SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation","created":"2024-08-10","cited_by_count":1,"area":null},{"id":"2411.17584","title":"Pre-training for Action Recognition with Automatically Generated Fractal Datasets","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2504.10201","title":"VibrantLeaves: A principled parametric image generator for training deep restoration models","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2506.19465","title":"Stylized Structural Patterns for Improved Neural Network Pre-training","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2510.24159","title":"Self-supervised Synthetic Pretraining for Inference of Stellar Mass Embedded in Dense Gas","created":"2025-10-28","cited_by_count":1,"area":null},{"id":"2511.01817","title":"SciTextures: Collecting and Connecting Visual Patterns, Models, and Code Across Science and Art","created":"2025-11-03","cited_by_count":1,"area":null},{"id":"2111.00124","title":"Predicting Atlantic Multidecadal Variability","created":"2021-10-29","cited_by_count":0,"area":null},{"id":"2205.02162","title":"UnrealNAS: Can We Search Neural Architectures with Unreal Data?","created":"2022-05-04","cited_by_count":0,"area":null},{"id":"2305.13509","title":"ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2403.08592","title":"Data-Efficient Sleep Staging with Synthetic Time Series Pretraining","created":"2024-03-13","cited_by_count":0,"area":null},{"id":"2405.06980","title":"Fractals as Pre-training Datasets for Anomaly Detection and Localization","created":"2024-05-11","cited_by_count":0,"area":null},{"id":"2407.02248","title":"EvolBA: Evolutionary Boundary Attack under Hard-label Black Box condition","created":"2024-07-02","cited_by_count":0,"area":null},{"id":"2408.15722","title":"Advanced POD-Based Performance Evaluation of Classifiers Applied to Human Driver Lane Changing Prediction","created":"2024-08-28","cited_by_count":0,"area":null},{"id":"2409.08884","title":"Detect Fake with Fake: Leveraging Synthetic Data-driven Representation for Synthetic Image Detection","created":"2024-09-13","cited_by_count":0,"area":null},{"id":"2502.01490","title":"MoireDB: Formula-generated Interference-fringe Image Dataset","created":"2025-02-03","cited_by_count":0,"area":null},{"id":"2504.04428","title":"Formula-Supervised Sound Event Detection: Pre-Training Without Real Data","created":"2025-04-06","cited_by_count":0,"area":null},{"id":"2505.13099","title":"Industrial Synthetic Segment Pre-training","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2508.11697","title":"Separating Knowledge and Perception with Procedural Data","created":"2025-08-12","cited_by_count":0,"area":null},{"id":"2509.00903","title":"Learning with Mandelbrot and Julia","created":"2025-08-31","cited_by_count":0,"area":null},{"id":"2510.23414","title":"Symmetria: A Synthetic Dataset for Learning in Point Clouds","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2602.11810","title":"How to Sample High Quality 3D Fractals for Action Recognition Pre-Training?","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2603.23199","title":"FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2603.25109","title":"MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2606.10802","title":"Boosting ECG Classification Performance by Pre-training with Synthesized Data","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.20347","title":"Critical Percolation as a Synthetic Data Model for Interpretability","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2608.24175","title":"Amortized Set Prediction for Inverse IFS Reconstruction from Density Maps","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.15067","title":"Rethinking Procedural Audio Pre-training: Source Scaling and Objective Adaptation","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2609.17831","title":"Procedural Pretraining for Molecular Property Prediction","created":"2026-09-15","cited_by_count":0,"area":null}],"next_cursor":null}