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 3201–3250 of 6661 papers

TitleStatusHype
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
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding—0
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks—0
Fuse after Align: Improving Face-Voice Association Learning via Multimodal Encoder—0
INDUS: Effective and Efficient Language Models for Scientific Applications—0
Function Contrastive Learning of Transferable Meta-Representations—0
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning—0
Function Contrastive Learning of Transferable Representations—0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—0
Channel-Wise Contrastive Learning for Learning with Noisy Labels—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Contrastive Learning for Time Series on Dynamic Graphs—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
Information-Aware Time Series Meta-Contrastive Learning—0
Learning Compact and Robust Representations for Anomaly Detection—0
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification—0
Information-guided pixel augmentation for pixel-wise contrastive learning—0
Information Maximization for Extreme Pose Face Recognition—0
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach—0
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency—0
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory—0
3D-Aware Encoding for Style-based Neural Radiance Fields—0
Injecting Text in Self-Supervised Speech Pretraining—0
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search—0
Learning Genomic Structure from k-mers—0
Learning Hidden Subgoals under Temporal Ordering Constraints in Reinforcement Learning—0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning—0
Learning Invariant Representation via Contrastive Feature Alignment for Clutter Robust SAR Target Recognition—0
Learning long-term music representations via hierarchical contextual constraints—0
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER—0
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing—0
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data—0
Instance Segmentation with Cross-Modal Consistency—0
Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation—0
From Patches to Objects: Exploiting Spatial Reasoning for Better Visual Representations—0
Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection—0
Integrating Auxiliary Information in Self-supervised Learning—0
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis—0
Integrating Continuous and Binary Relevances in Audio-Text Relevance Learning—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Contrastive Learning for Regression on Hyperspectral Data—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