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 3251–3275 of 6661 papers

TitleStatusHype
Zero-shot domain adaptation based on dual-level mix and contrast—0
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers—0
ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation—0
Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive LearningCode0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
TopoGCL: Topological Graph Contrastive LearningCode0
Data curation via joint example selection further accelerates multimodal learning—0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation LearningCode0
Enhancing OOD Detection Using Latent DiffusionCode0
The Championship-Winning Solution for the 5th CLVISION Challenge 2024—0
Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments—0
Self-Supervised Alignment Learning for Medical Image Segmentation—0
Fine-grained Background Representation for Weakly Supervised Semantic SegmentationCode0
Speech Analysis of Language Varieties in ItalyCode0
DN-CL: Deep Symbolic Regression against Noise via Contrastive Learning—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss—0
A Contrastive Learning Approach to Mitigate Bias in Speech ModelsCode0
Revealing Vision-Language Integration in the Brain with Multimodal NetworksCode0
Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes—0
Factual Dialogue Summarization via Learning from Large Language Models—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