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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5276–5300 of 6661 papers

TitleStatusHype
Incorporating granularity bias as the margin into contrastive loss for video captioning—0
Incremental False Negative Detection for Contrastive Learning—0
Indoor Smartphone SLAM with Learned Echoic Location Features—0
Inductive-Biases for Contrastive Learning of Disentangled Representations—0
INDUS: Effective and Efficient Language Models for Scientific Applications—0
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
InfoNCE: Identifying the Gap Between Theory and Practice—0
Information-Aware Time Series Meta-Contrastive Learning—0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Information-guided pixel augmentation for pixel-wise contrastive learning—0
Information Maximization for Extreme Pose Face Recognition—0
Addressing Feature Suppression in Unsupervised Visual Representations—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Injecting Text in Self-Supervised Speech Pretraining—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input—0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning—0
Instance Paradigm Contrastive Learning for Domain Generalization—0
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning—0
Instance Segmentation with Cross-Modal Consistency—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