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 601–625 of 6661 papers

TitleStatusHype
Temperature-Free Loss Function for Contrastive Learning—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
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application—0
Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction—0
Hypergraph Diffusion for High-Order Recommender Systems—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
Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer—0
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language PretrainingCode2
NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose EstimationCode0
Episodic Novelty Through Temporal Distance—0
Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language ModelsCode2
Reliable Pseudo-labeling via Optimal Transport with Attention for Short Text ClusteringCode0
E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic ExpressionsCode0
Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image UnderstandingCode2
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential RecommendationCode1
Low-rank Prompt Interaction for Continual Vision-Language RetrievalCode1
Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation—0
MCRL4OR: Multimodal Contrastive Representation Learning for Off-Road Environmental PerceptionCode0
Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer—0
MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender SystemsCode1
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion 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