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 3351–3375 of 6661 papers

TitleStatusHype
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search—0
UniSync: A Unified Framework for Audio-Visual Synchronization—0
Uniting contrastive and generative learning for event sequences models—0
Universal hidden monotonic trend estimation with contrastive learning—0
Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning—0
Unleashing Potential of Unsupervised Pre-Training With Intra-Identity Regularization for Person Re-Identification—0
Unleashing the Power of LLMs as Multi-Modal Encoders for Text and Graph-Structured Data—0
Unlocking the Capabilities of Vision-Language Models for Generalizable and Explainable Deepfake Detection—0
Enhancing Multimodal Unified Representations for Cross Modal Generalization—0
Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning—0
Unpaired Deep Image Deraining Using Dual Contrastive Learning—0
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning—0
Unpaired MRI Super Resolution with Contrastive Learning—0
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations—0
Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage—0
UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal—0
Unsupervised Active Pre-Training for Reinforcement Learning—0
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision—0
Unsupervised Audio-Visual Segmentation with Modality Alignment—0
Unsupervised CD in satellite image time series by contrastive learning and feature tracking—0
Fully Unsupervised Person Re-identification viaSelective Contrastive Learning—0
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation—0
Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection—0
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift—0
Unsupervised Contrastive Learning for Signal-Dependent Noise Synthesis—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