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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 2801–2850 of 6661 papers

TitleStatusHype
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging—0
Graph Contrastive Pre-training for Effective Theorem Reasoning—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
GLCC: A General Framework for Graph-Level Clustering—0
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization—0
Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification—0
Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning—0
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics—0
Contrastive Learning with Positive-Negative Frame Mask for Music Representation—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Graph Representation Learning via Contrasting Cluster Assignments—0
Graph Self-Contrast Representation Learning—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation—0
Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Framework—0
GraphTTA: Test Time Adaptation on Graph Neural Networks—0
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning—0
Incorporating Dense Knowledge Alignment into Unified Multimodal Representation Models—0
Contrastive Learning of Sentence Representations—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation—0
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization—0
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance—0
Grounded Language Acquisition From Object and Action Imagery—0
A Chinese Spelling Check Framework Based on Reverse Contrastive Learning—0
Grounding is All You Need? Dual Temporal Grounding for Video Dialog—0
Group-based Distinctive Image Captioning with Memory Attention—0
Group-based Distinctive Image Captioning with Memory Difference Encoding and Attention—0
Group Contrastive Self-Supervised Learning on Graphs—0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
Contrastive Learning of Preferences with a Contextual InfoNCE Loss—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
Adaptive Discriminative Regularization for Visual Classification—0
Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning—0
Incomplete Multi-view Clustering via Diffusion Completion—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
Contrastive Learning of Natural Language and Code Representations for Semantic Code Search—0
ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation—0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
Contrastive Learning for Image Complexity Representation—0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—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