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 1076–1100 of 6661 papers

TitleStatusHype
Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification—0
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance—0
A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target InteractionCode0
Dynamic Contrastive Learning for Time Series RepresentationCode0
Tensor-Fused Multi-View Graph Contrastive LearningCode0
LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space—0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model—0
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling—0
UniMTS: Unified Pre-training for Motion Time SeriesCode1
Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification—0
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning—0
Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection FrameworkCode0
GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence EmbeddingsCode0
Self-supervised contrastive learning performs non-linear system identificationCode1
DRACO-DehazeNet: An Efficient Image Dehazing Network Combining Detail Recovery and a Novel Contrastive Learning Paradigm—0
Preview-based Category Contrastive Learning for Knowledge Distillation—0
Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification PerformanceCode0
MACK: Mismodeling Addressed with Contrastive Knowledge—0
SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote SensingCode1
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment—0
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language ModelsCode2
FAMSeC: A Few-shot-sample-based General AI-generated Image Detection Method—0
Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive LearningCode0
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation—0
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning—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