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 2376–2400 of 6661 papers

TitleStatusHype
RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning—0
FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction—0
TopoCL: Topological Contrastive Learning for Time Series—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
Multi-level Supervised Contrastive Learning—0
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation—0
Mask-informed Deep Contrastive Incomplete Multi-view ClusteringCode0
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction—0
Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning—0
Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning—0
Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied AgentsCode0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor TypesCode0
BC-GAN: A Generative Adversarial Network for Synthesizing a Batch of Collocated Clothing—0
VisTA: Vision-Text Alignment Model with Contrastive Learning using Multimodal Data for Evidence-Driven, Reliable, and Explainable Alzheimer's Disease Diagnosis—0
neuro2voc: Decoding Vocalizations from Neural ActivityCode0
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—0
Contrastive Forward-Forward: A Training Algorithm of Vision Transformer—0
Improving vision-language alignment with graph spiking hybrid Networks—0
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution—0
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition—0
A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series—0
Sebra: Debiasing Through Self-Guided Bias RankingCode0
Deconstruct Complexity (DeComplex): A Novel Perspective on Tackling Dense Action Detection—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