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SwAV

Swapping Assignments between Views

GeneralIntroduced 200055 papers

Description

SwaV, or Swapping Assignments Between Views, is a self-supervised learning approach that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, it simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, SwaV uses a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view.

Papers Using This Method

Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach2025-06-08 Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare2025-05-22 Is Self-Supervised Pre-training on Satellite Imagery Better than ImageNet? A Systematic Study with Sentinel-22025-02-15 Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling2025-01-29 Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis2025-01-21 Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database2025-01-20 Diagnosis and Severity Assessment of Ulcerative Colitis using Self Supervised Learning2024-12-09 SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation2024-10-15 Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation2024-05-29 Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification2024-03-08 SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning2024-01-24 Survival Analysis of Young Triple-Negative Breast Cancer Patients2024-01-15 Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing2024-01-11 Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend2023-11-22 SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA2023-10-10 SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR2023-08-15 The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning2023-07-20 Semi-supervised learning made simple with self-supervised clustering2023-06-13 Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning2023-06-05 Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video2023-05-04