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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1225 of 6661 papers

TitleStatusHype
Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity—0
DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation—0
Patch-Based Contrastive Learning and Memory Consolidation for Online Unsupervised Continual LearningCode0
ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic FeaturesCode0
Self-Supervised Any-Point Tracking by Contrastive Random WalksCode2
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation—0
Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction—0
Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting—0
TextToon: Real-Time Text Toonify Head Avatar from Single Video—0
TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models—0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Learning from Contrastive Prompts: Automated Optimization and Adaptation—0
Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment—0
Scene-Text Grounding for Text-Based Video Question AnsweringCode1
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading ComprehensionCode0
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-TrainingCode0
Vision-Language Models Assisted Unsupervised Video Anomaly Detection—0
Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive LearningCode0
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance—0
ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning—0
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models—0
Recent Advancement of Emotion Cognition in Large Language Models—0
RingMo-Aerial: An Aerial Remote Sensing Foundation Model With A Affine Transformation Contrastive Learning—0
Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning—0
High-dimensional learning of narrow neural networks—0
Show:102550
← PrevPage 49 of 267Next →
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