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 5676–5700 of 6661 papers

TitleStatusHype
Making the Most of Text Semantics to Improve Biomedical Vision--Language ProcessingCode0
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation—0
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation—0
Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis—0
Caption Feature Space Regularization for Audio CaptioningCode0
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language UnderstandingCode0
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation—0
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval—0
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling—0
Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning—0
CroCo: Cross-Modal Contrastive learning for localization of Earth Observation dataCode0
Efficient Cluster-Based k-Nearest-Neighbor Machine TranslationCode0
Contrastive Learning for Image Registration in Visual Teach and Repeat NavigationCode0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning—0
Augmentation-Free Graph Contrastive Learning with Performance Guarantee—0
A Token-level Contrastive Framework for Sign Language TranslationCode0
Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels—0
Self-Supervised Video Representation Learning with Motion-Contrastive Perception—0
Robust Cross-Modal Representation Learning with Progressive Self-Distillation—0
Probabilistic Representations for Video Contrastive Learning—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