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 2276–2300 of 6661 papers

TitleStatusHype
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering—0
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—0
Decentralized Collective World Model for Emergent Communication and Coordination—0
Debiasing Multimodal Sarcasm Detection with Contrastive Learning—0
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation—0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis—0
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector—0
Debiased Novel Category Discovering and Localization—0
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data—0
Debiased Model-based Interactive Recommendation—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge—0
A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images—0
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
Contrastive Representation Disentanglement for Clustering—0
Debiased Contrastive Learning of Unsupervised Sentence Representations—0
(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)—0
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications—0
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective—0
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis—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