{"list":"other","total":52,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"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":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"id":"1708.04552","title":"Improved Regularization of Convolutional Neural Networks with Cutout","created":"2017-08-15","cited_by_count":1628},{"id":"1708.02072","title":"Measuring Catastrophic Forgetting in Neural Networks","created":"2017-08-07","cited_by_count":360},{"id":"1703.08475","title":"Overcoming Catastrophic Forgetting by Incremental Moment Matching","created":"2017-03-24","cited_by_count":243},{"id":"2207.10666","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers","created":"2022-07-21","cited_by_count":234},{"id":"2101.04898","title":"Unlearnable Examples: Making Personal Data Unexploitable","created":"2021-01-13","cited_by_count":193},{"id":"2210.11369","title":"On Feature Learning in the Presence of Spurious Correlations","created":"2022-10-20","cited_by_count":145},{"id":"2007.06778","title":"An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models","created":"2020-07-14","cited_by_count":134},{"id":"2004.00225","title":"MetaPoison: Practical General-purpose Clean-label Data Poisoning","created":"2020-04-01","cited_by_count":120},{"id":"2111.00898","title":"Availability Attacks Create Shortcuts","created":"2021-11-01","cited_by_count":62},{"id":"2111.13445","title":"How Well Do Sparse Imagenet Models Transfer?","created":"2021-11-26","cited_by_count":50},{"id":"2206.03693","title":"Autoregressive Perturbations for Data Poisoning","created":"2022-06-08","cited_by_count":46},{"id":"2311.13127","title":"MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning","created":"2023-11-22","cited_by_count":44},{"id":"2210.10114","title":"Transferable Unlearnable Examples","created":"2022-10-18","cited_by_count":43},{"id":"2301.13838","title":"Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression","created":"2023-01-31","cited_by_count":39},{"id":"2306.01902","title":"Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation","created":"2023-06-02","cited_by_count":37},{"id":"2301.01217","title":"Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples","created":"2022-12-31","cited_by_count":36},{"id":"2203.14533","title":"Robust Unlearnable Examples: Protecting Data Against Adversarial Learning","created":"2022-03-28","cited_by_count":35},{"id":"2301.04644","title":"Does progress on ImageNet transfer to real-world datasets?","created":"2023-01-11","cited_by_count":35},{"id":"2202.11202","title":"Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning","created":"2022-02-22","cited_by_count":34},{"id":"2303.15127","title":"Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks","created":"2023-03-27","cited_by_count":30},{"id":"2205.12141","title":"One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks","created":"2022-05-24","cited_by_count":29},{"id":"2303.04278","title":"CUDA: Convolution-based Unlearnable Datasets","created":"2023-03-07","cited_by_count":26},{"id":"2305.19254","title":"What Can We Learn from Unlearnable Datasets?","created":"2023-05-30","cited_by_count":21},{"id":"2502.06808","title":"On the Benefits of Attribute-Driven Graph Domain Adaptation","created":"2025-02-04","cited_by_count":19},{"id":"2405.03316","title":"Provably Unlearnable Data Examples","created":"2024-05-06","cited_by_count":18},{"id":"2209.00793","title":"Structure-Preserving Graph Representation Learning","created":"2022-09-02","cited_by_count":17},{"id":"2212.06254","title":"You Only Need a Good Embeddings Extractor to Fix Spurious Correlations","created":"2022-12-12","cited_by_count":17},{"id":"2410.09909","title":"UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation","created":"2024-10-13","cited_by_count":14},{"id":"2412.09026","title":"Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model","created":"2024-12-12","cited_by_count":14},{"id":"2401.17523","title":"Game-Theoretic Unlearnable Example Generator","created":"2024-01-31","cited_by_count":12},{"id":"2402.04010","title":"Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously","created":"2024-02-06","cited_by_count":12},{"id":"2505.05279","title":"MTL-UE: Learning to Learn Nothing for Multi-Task Learning","created":"2025-05-08","cited_by_count":11},{"id":"2505.20089","title":"Homophily Enhanced Graph Domain Adaptation","created":"2025-05-26","cited_by_count":9},{"id":"2402.12626","title":"Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors","created":"2024-02-20","cited_by_count":8},{"id":"2407.11011","title":"Toward Availability Attacks in 3D Point Clouds","created":"2024-06-26","cited_by_count":7},{"id":"2309.09466","title":"Progressive Text-to-Image Diffusion with Soft Latent Direction","created":"2023-09-18","cited_by_count":6},{"id":"2411.17113","title":"Learning from Noisy Labels via Conditional Distributionally Robust Optimization","created":"2024-11-26","cited_by_count":6},{"id":"2405.20790","title":"Intersectional Unfairness Discovery","created":"2024-05-31","cited_by_count":5},{"id":"2509.08058","title":"How Far Are We from True Unlearnability?","created":"2025-09-09","cited_by_count":4},{"id":"2306.14460","title":"Hierarchical Matching and Reasoning for Multi-Query Image Retrieval","created":"2023-06-26","cited_by_count":3},{"id":"2412.21061","title":"BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection","created":"2024-12-30","cited_by_count":3}],"next_cursor":null}