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 4151–4175 of 6661 papers

TitleStatusHype
Consensus-aware Contrastive Learning for Group Recommendation—0
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning—0
Consistency Enhancement-Based Deep Multiview Clustering via Contrastive Learning—0
Consistent Assignment for Representation Learning—0
Consistent Subject Generation via Contrastive Instantiated Concepts—0
ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning—0
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—0
Constrained Multiview Representation for Self-supervised Contrastive Learning—0
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning—0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
Context-Aware Multimodal Pretraining—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
Context-aware Video Anomaly Detection in Long-Term Datasets—0
Context-aware Visual Storytelling with Visual Prefix Tuning and Contrastive Learning—0
ContextCLIP: Contextual Alignment of Image-Text pairs on CLIP visual representations—0
Incorporating simulated spatial context information improves the effectiveness of contrastive learning models—0
Context-invariant, multi-variate time series representations—0
Contextrast: Contextual Contrastive Learning for Semantic Segmentation—0
Contextual Augmented Global Contrast for Multimodal Intent Recognition—0
Contextual Document Embeddings—0
Contextual Outpainting With Object-Level Contrastive Learning—0
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification—0
Continual Contrastive Finetuning Improves Low-Resource Relation 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