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 5651–5700 of 6661 papers

TitleStatusHype
Metadata-enhanced contrastive learning from retinal optical coherence tomography images—0
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs—0
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation—0
Metapath-based Hyperbolic Contrastive Learning for Heterogeneous Graph Embedding—0
Meta-ZSDETR: Zero-shot DETR with Meta-learning—0
Metric-based multimodal meta-learning for human movement identification via footstep recognition—0
Metric Learning for 3D Point Clouds Using Optimal Transport—0
MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting—0
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging—0
MGS3: A Multi-Granularity Self-Supervised Code Search Framework—0
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval—0
Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning—0
MIM4DD: Mutual Information Maximization for Dataset Distillation—0
MimCo: Masked Image Modeling Pre-training with Contrastive Teacher—0
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition—0
Mind Your Clever Neighbours: Unsupervised Person Re-identification via Adaptive Clustering Relationship Modeling—0
Mining Better Samples for Contrastive Learning of Temporal Correspondence—0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction—0
MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation—0
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning—0
Misinformation Detection in Social Media Video Posts—0
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction—0
MiSS@WMT21: Contrastive Learning-reinforced Domain Adaptation in Neural Machine Translation—0
Mitigating Catastrophic Forgetting in Task-Incremental Continual Learning with Adaptive Classification Criterion—0
Mitigating Contradictions in Dialogue Based on Contrastive Learning—0
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization—0
Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning—0
Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations—0
Mitigating Human and Computer Opinion Fraud via Contrastive Learning—0
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy—0
Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training—0
MixCL: Pixel label matters to contrastive learning—0
Mixed Preference Optimization: Reinforcement Learning with Data Selection and Better Reference Model—0
Mixed Supervised Graph Contrastive Learning for Recommendation—0
MixSiam: A Mixture-based Approach to Self-supervised Representation Learning—0
MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning—0
MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning—0
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding—0
MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT—0
Multilingual Molecular Representation Learning via Contrastive Pre-training—0
MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
MN-Pair Contrastive Damage Representation and Clustering for Prognostic Explanation—0
MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation—0
MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling—0
MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion—0
MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains—0
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration—0
ModEFormer: Modality-Preserving Embedding for Audio-Video Synchronization using Transformers—0
Model and Evaluation: Towards Fairness in Multilingual 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