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 5026–5050 of 6661 papers

TitleStatusHype
GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization—0
GLCC: A General Framework for Graph-Level Clustering—0
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning—0
Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision—0
GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
Goal-conditioned reinforcement learning for ultrasound navigation guidance—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder—0
Graded Relevance Scoring of Written Essays with Dense Retrieval—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation—0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation—0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Graph Contrastive Learning for Materials—0
Graph Contrastive Learning for Multi-omics Data—0
Graph Contrastive Learning on Multi-label Classification for Recommendations—0
Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification—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