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 2601–2625 of 6661 papers

TitleStatusHype
Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis—0
Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning—0
Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection—0
Contrastive Video Textures—0
Contrastive Video-Language Segmentation—0
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?—0
Contrastive Unsupervised Learning for Audio Fingerprinting—0
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives—0
Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training—0
Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models—0
Can representation learning for multimodal image registration be improved by supervision of intermediate layers?—0
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training—0
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Can Contrastive Learning Refine Embeddings—0
Contrastive String Representation Learning using Synthetic Data—0
A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction—0
Human-aligned Deep Learning: Explainability, Causality, and Biological Inspiration—0
Contrastive Speaker Embedding With Sequential Disentanglement—0
Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs—0
Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework—0
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces—0
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification—0
Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation—0
Approximate Bayesian Computation via Classification—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