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 1101–1125 of 6661 papers

TitleStatusHype
CMAL: A Novel Cross-Modal Associative Learning Framework for Vision-Language Pre-Training—0
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images—0
Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data—0
Contrastive learning of cell state dynamics in response to perturbationsCode2
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning—0
SeaDATE: Remedy Dual-Attention Transformer with Semantic Alignment via Contrast Learning for Multimodal Object Detection—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal ContrastCode0
Querying functional and structural niches on spatial transcriptomics dataCode0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
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
BrainMVP: Multi-modal Vision Pre-training for Brain Image Analysis using Multi-parametric MRICode2
Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention—0
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation—0
ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification—0
Large-Scale 3D Medical Image Pre-training with Geometric Context PriorsCode3
t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving—0
Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid ViewsCode1
Pic@Point: Cross-Modal Learning by Local and Global Point-Picture Correspondence—0
Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question AnsweringCode1
Contrastive Learning for Implicit Social Factors in Social Media Popularity PredictionCode0
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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