{"list":"other","total":54,"rows":[{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2212.09748","title":"Scalable Diffusion Models with Transformers","created":"2022-12-19","cited_by_count":6054},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"1912.04958","title":"Analyzing and Improving the Image Quality of StyleGAN","created":"2019-12-03","cited_by_count":3588},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"2006.05929","title":"Dataset Condensation with Gradient Matching","created":"2020-06-10","cited_by_count":516},{"id":"2203.11932","title":"Dataset Distillation by Matching Training Trajectories","created":"2022-03-22","cited_by_count":442},{"id":"2302.07121","title":"Universal Guidance for Diffusion Models","created":"2023-02-14","cited_by_count":437},{"id":"2110.04596","title":"Deep Long-Tailed Learning: A Survey","created":"2021-10-09","cited_by_count":417},{"id":"2110.04181","title":"Dataset Condensation with Distribution Matching","created":"2021-10-08","cited_by_count":365},{"id":"2303.09833","title":"FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model","created":"2023-03-17","cited_by_count":294},{"id":"2406.08177","title":"One-Step Effective Diffusion Network for Real-World Image Super-Resolution","created":"2024-06-12","cited_by_count":246},{"id":"2311.14760","title":"SinSR: Diffusion-Based Image Super-Resolution in a Single Step","created":"2023-11-23","cited_by_count":238},{"id":"1904.01186","title":"Data-Free Learning of Student Networks","created":"2019-04-02","cited_by_count":204},{"id":"2211.11004","title":"Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation","created":"2022-11-20","cited_by_count":142},{"id":"2306.13092","title":"Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective","created":"2023-06-22","cited_by_count":138},{"id":"2207.00050","title":"Semantic Image Synthesis via Diffusion Models","created":"2022-06-30","cited_by_count":132},{"id":"2301.07014","title":"Dataset Distillation: A Comprehensive Review","created":"2023-01-17","cited_by_count":127},{"id":"2305.01649","title":"Generalizing Dataset Distillation via Deep Generative Prior","created":"2023-05-02","cited_by_count":126},{"id":"2310.05773","title":"Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching","created":"2023-10-09","cited_by_count":118},{"id":"2312.03526","title":"On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm","created":"2023-12-06","cited_by_count":109},{"id":"1912.07768","title":"Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data","created":"2019-12-17","cited_by_count":104},{"id":"2412.09013","title":"Arbitrary-steps Image Super-resolution via Diffusion Inversion","created":"2024-12-12","cited_by_count":86},{"id":"2310.00093","title":"DataDAM: Efficient Dataset Distillation with Attention Matching","created":"2023-09-29","cited_by_count":84},{"id":"2311.15529","title":"Efficient Dataset Distillation via Minimax Diffusion","created":"2023-11-27","cited_by_count":83},{"id":"2407.15138","title":"D$^4$M: Dataset Distillation via Disentangled Diffusion Model","created":"2024-07-21","cited_by_count":77},{"id":"2311.17950","title":"Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching","created":"2023-11-29","cited_by_count":69},{"id":"2302.06755","title":"Dataset Distillation with Convexified Implicit Gradients","created":"2023-02-13","cited_by_count":46},{"id":"2502.20653","title":"Dataset Distillation with Neural Characteristic Function: A Minmax Perspective","created":"2025-02-28","cited_by_count":45},{"id":"2404.00563","title":"Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation","created":"2024-03-31","cited_by_count":31},{"id":"2406.18561","title":"SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching","created":"2024-05-28","cited_by_count":22},{"id":"2405.11525","title":"Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors","created":"2024-05-19","cited_by_count":21},{"id":"2406.19827","title":"Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory","created":"2024-06-28","cited_by_count":21},{"id":"2406.07876","title":"Small Scale Data-Free Knowledge Distillation","created":"2024-06-12","cited_by_count":20},{"id":"2502.06808","title":"On the Benefits of Attribute-Driven Graph Domain Adaptation","created":"2025-02-04","cited_by_count":19},{"id":"2403.07294","title":"Graph Data Condensation via Self-expressive Graph Structure Reconstruction","created":"2024-03-12","cited_by_count":18},{"id":"2410.17193","title":"Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios","created":"2024-10-22","cited_by_count":18},{"id":"2505.18399","title":"Taming Diffusion for Dataset Distillation with High Representativeness","created":"2025-05-23","cited_by_count":18},{"id":"2209.00793","title":"Structure-Preserving Graph Representation Learning","created":"2022-09-02","cited_by_count":17},{"id":"2406.01063","title":"DANCE: Dual-View Distribution Alignment for Dataset Condensation","created":"2024-06-03","cited_by_count":15},{"id":"2505.13300","title":"DD-Ranking: Rethinking the Evaluation of Dataset Distillation","created":"2025-05-19","cited_by_count":10},{"id":"2408.14506","title":"Distilling Long-tailed Datasets","created":"2024-08-24","cited_by_count":9},{"id":"2503.12821","title":"From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration","created":"2025-03-17","cited_by_count":8},{"id":"2412.18557","title":"FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis","created":"2024-12-24","cited_by_count":6},{"id":"2309.07666","title":"Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning","created":"2023-09-14","cited_by_count":5},{"id":"2405.17535","title":"Calibrated Dataset Condensation for Faster Hyperparameter Search","created":"2024-05-27","cited_by_count":5},{"id":"2501.14216","title":"TFG-Flow: Training-free Guidance in Multimodal Generative Flow","created":"2025-01-24","cited_by_count":5},{"id":"2602.07573","title":"Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure","created":"2026-02-07","cited_by_count":3},{"id":"2605.02202","title":"CBV: Clean-label Backdoor Attacks on Vision Language Models via Diffusion Models","created":"2026-05-04","cited_by_count":3},{"id":"2509.13858","title":"EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics","created":"2025-09-17","cited_by_count":1}],"next_cursor":null}