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 3651–3700 of 6661 papers

TitleStatusHype
Generalization Bounds for Adversarial Contrastive Learning—0
Generalized Class Discovery in Instance Segmentation—0
Generalizing Graph ODE for Learning Complex System Dynamics across Environments—0
Generalizing Supervised Contrastive learning: A Projection Perspective—0
General-Purpose Multi-Modal OOD Detection Framework—0
Generating Compositional Color Representations from Text—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text—0
Exploiting Auxiliary Caption for Video Grounding—0
Generative Adversarial Learning via Kernel Density Discrimination—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits—0
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input—0
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
Generative Modeling of Class Probability for Multi-Modal Representation Learning—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
GenURL: A General Framework for Unsupervised Representation Learning—0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
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
Graph Contrastive Learning with Generative Adversarial Network—0
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search—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