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 3651–3675 of 6661 papers

TitleStatusHype
Deep Contrastive Multi-view Clustering under Semantic Feature Guidance—0
Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification—0
Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning—0
Spectrum Translation for Refinement of Image Generation (STIG) Based on Contrastive Learning and Spectral Filter ProfileCode0
ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning—0
Poly-View Contrastive Learning—0
Text-to-Audio Generation Synchronized with Videos—0
Enhancing Multimodal Unified Representations for Cross Modal Generalization—0
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global WorkspaceCode0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
ACC-ViT : Atrous Convolution's Comeback in Vision Transformers—0
UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed EntitiesCode0
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition—0
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision—0
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition—0
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation—0
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift—0
Intent-aware Recommendation via Disentangled Graph Contrastive Learning—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Contrastive Pre-training for Deep Session Data Understanding—0
Multi-Scale Subgraph Contrastive Learning—0
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation—0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
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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