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 2076–2100 of 6661 papers

TitleStatusHype
Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning—0
Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion—0
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences—0
Direction-Aware Hybrid Representation Learning for 3D Hand Pose and Shape Estimation—0
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition—0
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement—0
Directed Link Prediction using GNN with Local and Global Feature Fusion—0
Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection—0
DINeMo: Learning Neural Mesh Models with no 3D Annotations—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning—0
DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation—0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data—0
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation—0
FewUser: Few-Shot Social User Geolocation via Contrastive Learning—0
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser—0
Diffusion Models as Masked Audio-Video Learners—0
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation—0
Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity—0
Diffusion-augmented Graph Contrastive Learning for Collaborative Filter—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—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