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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 1351–1400 of 6661 papers

TitleStatusHype
Alleviating Exposure Bias via Contrastive Learning for Abstractive Text SummarizationCode1
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked AutoencodersCode1
Extending global-local view alignment for self-supervised learning with remote sensing imageryCode1
Contrastive Learning of Generalized Game RepresentationsCode1
Network Comparison with Interpretable Contrastive Network Representation LearningCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
Direct Preference-based Policy Optimization without Reward ModelingCode1
Multi-level Feature Learning for Contrastive Multi-view ClusteringCode1
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial LearningCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
Contrastive Learning of Musical RepresentationsCode1
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential RecommendationCode1
ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic SegmentationCode1
Jigsaw Clustering for Unsupervised Visual Representation LearningCode1
Joint Contrastive Learning for Unsupervised Domain AdaptationCode1
Joint Contrastive Learning with Infinite PossibilitiesCode1
Automated Essay Scoring via Pairwise Contrastive RegressionCode1
Contrastive Learning of Sentence Embeddings from ScratchCode1
An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language ModelsCode1
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-DistillationCode1
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion RecognitionCode1
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive LearningCode1
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph EmbeddingsCode1
KMM: Key Frame Mask Mamba for Extended Motion GenerationCode1
Contrastive Multimodal Fusion with TupleInfoNCECode1
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology ImagesCode1
Contrastive Multiview CodingCode1
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph CompletionCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP TrainingCode1
Disentangled Causal Embedding With Contrastive Learning For Recommender SystemCode1
DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare DataCode1
Differentiable Data Augmentation for Contrastive Sentence Representation LearningCode1
Language modeling via stochastic processesCode1
Language Models As Semantic IndexersCode1
DialogueCSE: Dialogue-based Contrastive Learning of Sentence EmbeddingsCode1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
From t-SNE to UMAP with contrastive learningCode1
Contrastive Meta Learning with Behavior Multiplicity for RecommendationCode1
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label ClassificationCode1
DiffSim: Taming Diffusion Models for Evaluating Visual SimilarityCode1
Contrastive Learning with Adversarial Perturbations for Conditional Text GenerationCode1
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive LearningCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Contrastive Learning with Boosted MemorizationCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI ClassificationCode1
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based LossesCode1
Compressive Visual RepresentationsCode1
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive LearningCode1
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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