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 2401–2425 of 6661 papers

TitleStatusHype
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Sebra: Debiasing Through Self-Guided Bias RankingCode0
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain—0
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches—0
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning—0
Temperature-Free Loss Function for Contrastive Learning—0
DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection—0
CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors—0
Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction—0
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application—0
Hypergraph Diffusion for High-Order Recommender Systems—0
NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose EstimationCode0
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks—0
Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Episodic Novelty Through Temporal Distance—0
Reliable Pseudo-labeling via Optimal Transport with Attention for Short Text ClusteringCode0
Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation—0
E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic ExpressionsCode0
Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer—0
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models—0
Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques—0
MCRL4OR: Multimodal Contrastive Representation Learning for Off-Road Environmental PerceptionCode0
2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings—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