{"total":40,"rows":[{"id":"2111.09886","title":"SimMIM: A Simple Framework for Masked Image Modeling","created":"2021-11-18","cited_by_count":1153,"area":null},{"id":"2301.05712","title":"A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends","created":"2023-01-13","cited_by_count":207,"area":null},{"id":"2105.00470","title":"On Feature Decorrelation in Self-Supervised Learning","created":"2021-05-02","cited_by_count":160,"area":null},{"id":"2105.04553","title":"Self-Supervised Learning with Swin Transformers","created":"2021-05-10","cited_by_count":122,"area":null},{"id":"2205.13543","title":"Revealing the Dark Secrets of Masked Image Modeling","created":"2022-05-26","cited_by_count":117,"area":null},{"id":"2106.03149","title":"Large-scale Unsupervised Semantic Segmentation","created":"2021-06-06","cited_by_count":101,"area":null},{"id":"2105.05837","title":"When Does Contrastive Visual Representation Learning Work?","created":"2021-05-12","cited_by_count":92,"area":null},{"id":"2105.08054","title":"Divide and Contrast: Self-supervised Learning from Uncurated Data","created":"2021-05-17","cited_by_count":85,"area":null},{"id":"2112.05141","title":"Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Framework","created":"2021-12-09","cited_by_count":49,"area":null},{"id":"2108.02982","title":"Improving Contrastive Learning by Visualizing Feature Transformation","created":"2021-08-06","cited_by_count":44,"area":null},{"id":"2202.00455","title":"HCSC: Hierarchical Contrastive Selective Coding","created":"2022-02-01","cited_by_count":43,"area":null},{"id":"2305.13689","title":"Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training","created":"2023-05-23","cited_by_count":39,"area":null},{"id":"2108.02183","title":"Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization","created":"2021-08-04","cited_by_count":27,"area":null},{"id":"2201.10728","title":"Training Vision Transformers with Only 2040 Images","created":"2022-01-26","cited_by_count":23,"area":null},{"id":"2304.00571","title":"DropMAE: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching Tasks","created":"2023-04-02","cited_by_count":22,"area":null},{"id":"2410.19560","title":"Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning","created":"2024-10-25","cited_by_count":20,"area":null},{"id":"2309.13439","title":"Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning","created":"2023-09-23","cited_by_count":18,"area":null},{"id":"2209.03063","title":"MimCo: Masked Image Modeling Pre-training with Contrastive Teacher","created":"2022-09-07","cited_by_count":16,"area":null},{"id":"2405.15018","title":"What Variables Affect Out-of-Distribution Generalization in Pretrained Models?","created":"2024-05-23","cited_by_count":13,"area":null},{"id":"2305.14376","title":"PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis","created":"2023-05-20","cited_by_count":10,"area":null},{"id":"2203.14370","title":"CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning","created":"2022-03-27","cited_by_count":9,"area":null},{"id":"2210.10194","title":"Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation","created":"2022-10-18","cited_by_count":9,"area":null},{"id":"2411.06508","title":"Understanding the Role of Equivariance in Self-supervised Learning","created":"2024-11-10","cited_by_count":7,"area":null},{"id":"2105.05838","title":"Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning","created":"2021-05-12","cited_by_count":6,"area":null},{"id":"2106.10137","title":"Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting","created":"2021-06-18","cited_by_count":6,"area":null},{"id":"2211.02043","title":"Could Giant Pretrained Image Models Extract Universal Representations?","created":"2022-11-03","cited_by_count":6,"area":null},{"id":"2302.01541","title":"Contrastive Learning with Consistent Representations","created":"2023-02-03","cited_by_count":6,"area":null},{"id":"2211.01796","title":"Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning","created":"2022-11-02","cited_by_count":4,"area":null},{"id":"2310.04551","title":"MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation","created":"2023-10-06","cited_by_count":4,"area":null},{"id":"2208.04268","title":"Label-Free Synthetic Pretraining of Object Detectors","created":"2022-08-08","cited_by_count":3,"area":null},{"id":"2210.13463","title":"Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future","created":"2022-10-23","cited_by_count":3,"area":null},{"id":"2102.04848","title":"Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning","created":"2021-02-09","cited_by_count":2,"area":null},{"id":"2301.03041","title":"Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning","created":"2023-01-08","cited_by_count":2,"area":null},{"id":"2304.00784","title":"Disentangled Pre-training for Image Matting","created":"2023-04-03","cited_by_count":2,"area":null},{"id":"2405.14669","title":"Efficiency for Free: Ideal Data Are Transportable Representations","created":"2024-05-23","cited_by_count":2,"area":null},{"id":"2104.00202","title":"Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning","created":"2021-04-01","cited_by_count":1,"area":null},{"id":"2208.00787","title":"On the robustness of self-supervised representations for multi-view object classification","created":"2022-07-27","cited_by_count":1,"area":null},{"id":"2310.18850","title":"Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models","created":"2023-10-28","cited_by_count":1,"area":null},{"id":"2404.19289","title":"On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning","created":"2024-04-30","cited_by_count":1,"area":null},{"id":"2206.06023","title":"Virtual embeddings and self-consistency for self-supervised learning","created":"2022-06-13","cited_by_count":0,"area":null}],"next_cursor":null}