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 6501–6525 of 6661 papers

TitleStatusHype
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding—0
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation—0
Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation—0
SPACL: Shared-Private Architecture based on Contrastive Learning for Multi-domain Text Classification—0
Sparse and Complete Latent Organization for Geospatial Semantic Segmentation—0
SparseCL: Sparse Contrastive Learning for Contradiction Retrieval—0
Sparse Contrastive Learning of Sentence Embeddings—0
Sparse Dictionary Learning by Dynamical Neural Networks—0
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST—0
Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization—0
Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics—0
Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification—0
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality—0
Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction—0
Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning—0
Spatio-temporal Contrastive Domain Adaptation for Action Recognition—0
Spatiotemporal Contrastive Learning for Cross-View Video Localization in Unstructured Off-road Terrains—0
Spatiotemporal Contrastive Learning of Facial Expressions in Videos—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition—0
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding—0
Speaker-Independent Dysarthria Severity Classification using Self-Supervised Transformers and Multi-Task Learning—0
Spectral Augmentations for Graph Contrastive Learning—0
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond—0
Spectral Temporal 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