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 5076–5100 of 6661 papers

TitleStatusHype
Group Reasoning Emission Estimation Networks—0
Adaptive Discriminative Regularization for Visual Classification—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation—0
GUIM -- General User and Item Embedding with Mixture of Representation in E-commerce—0
Hallucination Improves the Performance of Unsupervised Visual Representation Learning—0
HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals—0
Hard Negative Sampling Strategies for Contrastive Representation Learning—0
Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis—0
Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content From Parameterized Transformations—0
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Hashing based Contrastive Learning for Virtual Screening—0
HateProof: Are Hateful Meme Detection Systems really Robust?—0
HateSieve: A Contrastive Learning Framework for Detecting and Segmenting Hateful Content in Multimodal Memes—0
Hate Speech Detection via Dual Contrastive Learning—0
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation—0
HC^2L: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding—0
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
HCL-MTC Hierarchical Contrastive Learning for Multi-label Text Classification—0
HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies—0
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold—0
HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation—0
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization—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