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 551–575 of 6661 papers

TitleStatusHype
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning—0
O1 Embedder: Let Retrievers Think Before Action—0
Learning Clustering-based Prototypes for Compositional Zero-shot LearningCode1
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection—0
Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-IdentificationCode0
Structure-preserving contrastive learning for spatial time seriesCode0
RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion RecognitionCode0
Group Reasoning Emission Estimation Networks—0
Learning Street View Representations with Spatiotemporal ContrastCode0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Graph Contrastive Learning for Connectome ClassificationCode0
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE—0
Learning Temporal Invariance in Android Malware Detectors—0
Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning—0
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models—0
Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic Segmentation—0
Consistency of augmentation graph and network approximability in contrastive learningCode0
FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction—0
TopoCL: Topological Contrastive Learning for Time Series—0
RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning—0
Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion—0
SLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images—0
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation—0
Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning—0
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction—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