SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 3176–3200 of 6661 papers

TitleStatusHype
Contrastive learning for unsupervised medical image clustering and reconstruction—0
A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
Contrastive Learning for Unsupervised Image-to-Image Translation—0
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning—0
Fuse and Attend: Generalized Embedding Learning for Art and Sketches—0
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis—0
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt—0
Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation—0
Cross-Domain Document Layout Analysis Using Document Style Guide—0
Fuse after Align: Improving Face-Voice Association Learning via Multimodal Encoder—0
Incomplete Multi-view Clustering via Diffusion Completion—0
Incomplete Multi-view Clustering via Diffusion Contrastive Generation—0
Function Contrastive Learning of Transferable Meta-Representations—0
Function Contrastive Learning of Transferable Representations—0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Incorporating Dense Knowledge Alignment into Unified Multimodal Representation Models—0
Cross-domain Transfer of defect features in technical domains based on partial target data—0
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding—0
A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified