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 3926–3950 of 6661 papers

TitleStatusHype
Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics—0
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks—0
MIM4DD: Mutual Information Maximization for Dataset Distillation—0
scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive LearningCode0
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting—0
Medical Report Generation based on Segment-Enhanced Contrastive Representation Learning—0
Federated Hyperdimensional Computing—0
Masked Contrastive Reconstruction for Cross-modal Medical Image-Report Retrieval—0
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data LimitationsCode0
Understanding normalization in contrastive representation learning and out-of-distribution detectionCode0
Spatial-Temporal Decoupling Contrastive Learning for Skeleton-based Human Action RecognitionCode0
Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopment—0
A Language-based solution to enable Metaverse RetrievalCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision—0
Chasing Fairness in Graphs: A GNN Architecture PerspectiveCode0
Coreference Graph Guidance for Mind-Map GenerationCode0
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction—0
Time-Series Contrastive Learning against False Negatives and Class Imbalance—0
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers—0
CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding ResiduesCode0
SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action SegmentationCode0
Synergistic Anchored Contrastive Pre-training for Few-Shot Relation ExtractionCode0
Cross-Age Contrastive Learning for Age-Invariant Face Recognition—0
The Right Losses for the Right Gains: Improving the Semantic Consistency of Deep Text-to-Image Generation with Distribution-Sensitive Losses—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