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 4526–4550 of 6661 papers

TitleStatusHype
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes—0
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training—0
Del Visual al Auditivo: Sonorización de Escenas Guiada por Imagen—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
Dense Contrastive Visual-Linguistic Pretraining—0
Dense Semantic Contrast for Self-Supervised Visual Representation Learning—0
Density-Guided Semi-Supervised 3D Semantic Segmentation with Dual-Space Hardness Sampling—0
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles—0
DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition—0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—0
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning—0
Detect Low-Resource Rumors in Microblog Posts via Adversarial Contrastive Learning—0
Developing Healthcare Language Model Embedding Spaces—0
DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art—0
DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection—0
Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings—0
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling—0
DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation—0
Dialogue Response Generation via Contrastive Latent Representation Learning—0
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking—0
DICE: Data-Efficient Clinical Event Extraction with Generative Models—0
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning—0
Dictionary-based Framework for Interpretable and Consistent Object Parsing—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