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 6501–6550 of 6661 papers

TitleStatusHype
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
Enhancing Dialogue Generation via Multi-Level Contrastive Learning—0
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect DetectionCode1
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
MoPro: Webly Supervised Learning with Momentum PrototypesCode1
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA—0
The Next Big Thing(s) in Unsupervised Machine Learning: Five Lessons from Infant Learning—0
Group-wise Contrastive Learning for Neural Dialogue GenerationCode1
Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual LocalizationCode1
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations—0
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-IdentificationCode1
Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral ClassificationCode1
SAIL: Self-Augmented Graph Contrastive Learning—0
Active Contrastive Learning of Audio-Visual Video RepresentationsCode1
Learning Node Representations against PerturbationsCode0
Delving into Inter-Image Invariance for Unsupervised Visual RepresentationsCode2
Contrastive learning, multi-view redundancy, and linear models—0
VLANet: Video-Language Alignment Network for Weakly-Supervised Video Moment RetrievalCode1
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics—0
Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound—0
What Should Not Be Contrastive in Contrastive Learning—0
Self-supervised Video Representation Learning by Pace PredictionCode1
Spatiotemporal Contrastive Video Representation LearningCode1
Unsupervised Feature Learning by Cross-Level Instance-Group DiscriminationCode1
Deep Robust Clustering by Contrastive LearningCode1
Self-supervised Video Representation Learning Using Inter-intra Contrastive FrameworkCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Data Cleansing with Contrastive Learning for Vocal Note Event AnnotationsCode0
Temporal Context Aggregation for Video Retrieval with Contrastive LearningCode1
LoCo: Local Contrastive Representation Learning—0
SeCo: Exploring Sequence Supervision for Unsupervised Representation LearningCode1
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
Contrastive Learning for Unpaired Image-to-Image TranslationCode2
Distilling Visual Priors from Self-Supervised LearningCode1
Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action RecognitionCode1
A Visual Analytics Framework for Contrastive Network Analysis—0
On Learning Universal Representations Across Languages—0
Contrastive Learning for Unpaired Image-to-Image TranslationCode1
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—0
Self-Supervised Contrastive Learning for Unsupervised Phoneme SegmentationCode1
K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations—0
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
Contrastive Visual-Linguistic PretrainingCode0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation—0
Augmentation adversarial training for self-supervised speaker recognition—0
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning—0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
Hybrid Discriminative-Generative Training via Contrastive LearningCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—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