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 1476–1500 of 6661 papers

TitleStatusHype
Shapley Value-based Contrastive Alignment for Multimodal Information Extraction—0
Speed-enhanced Subdomain Adaptation Regression for Long-term Stable Neural Decoding in Brain-computer Interfaces—0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
Banyan: Improved Representation Learning with Explicit Structure—0
X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs—0
Intent-guided Heterogeneous Graph Contrastive Learning for RecommendationCode1
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
Multi-label Cluster Discrimination for Visual Representation LearningCode4
Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring—0
A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation—0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation—0
Balanced Multi-Relational Graph ClusteringCode0
Distribution-Aware Robust Learning from Long-Tailed Data with Noisy LabelsCode0
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift—0
Multi-Modality Co-Learning for Efficient Skeleton-based Action RecognitionCode1
NV-Retriever: Improving text embedding models with effective hard-negative mining—0
Learning at a Glance: Towards Interpretable Data-limited Continual Semantic Segmentation via Semantic-Invariance ModellingCode1
Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QACode0
Breaking the Global North Stereotype: A Global South-centric Benchmark Dataset for Auditing and Mitigating Biases in Facial Recognition Systems—0
Weak-to-Strong Compositional Learning from Generative Models for Language-based Object Detection—0
Self-supervised transformer-based pre-training method with General Plant Infection datasetCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual AlignmentCode0
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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