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 1151–1175 of 6661 papers

TitleStatusHype
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
WTCL-Dehaze: Rethinking Real-world Image Dehazing via Wavelet Transform and Contrastive Learning—0
Contrastive Learning to Improve Retrieval for Real-world Fact Checking—0
SimO Loss: Anchor-Free Contrastive Loss for Fine-Grained Supervised Contrastive Learning—0
Improving Object Detection via Local-global Contrastive Learning—0
Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture—0
Multi-Tiered Self-Contrastive Learning for Medical Microwave Radiometry (MWR) Breast Cancer DetectionCode0
Enhancement of Dysarthric Speech Reconstruction by Contrastive Learning—0
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss FunctionCode0
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
Improving Node Representation by Boosting Target-Aware Contrastive Loss—0
Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMs—0
CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation—0
Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series ForecastingCode0
SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual RepresentationsCode0
Contextual Document Embeddings—0
FARM: Functional Group-Aware Representations for Small Molecules—0
Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge TracingCode0
CktGen: Specification-Conditioned Analog Circuit Generation—0
ScVLM: Enhancing Vision-Language Model for Safety-Critical Event UnderstandingCode0
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset—0
Contrastive Abstraction for Reinforcement Learning—0
Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRICode0
NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion ModelsCode0
Decoding the Echoes of Vision from fMRI: Memory Disentangling for Past Semantic InformationCode0
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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