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 2826–2850 of 6661 papers

TitleStatusHype
CMAL: A Novel Cross-Modal Associative Learning Framework for Vision-Language Pre-Training—0
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning—0
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data—0
SeaDATE: Remedy Dual-Attention Transformer with Semantic Alignment via Contrast Learning for Multimodal Object Detection—0
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images—0
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
Querying functional and structural niches on spatial transcriptomics dataCode0
Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation—0
SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples—0
Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning PerspectiveCode0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal ContrastCode0
Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention—0
t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving—0
ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification—0
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation—0
Pic@Point: Cross-Modal Learning by Local and Global Point-Picture Correspondence—0
Bridging Text and Image for Artist Style Transfer via Contrastive Learning—0
Contrastive Learning for Implicit Social Factors in Social Media Popularity PredictionCode0
Towards Cross-domain Few-shot Graph Anomaly Detection—0
Intent-Enhanced Data Augmentation for Sequential Recommendation—0
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job RecommendationCode0
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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