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 3151–3175 of 6661 papers

TitleStatusHype
Speed-enhanced Subdomain Adaptation Regression for Long-term Stable Neural Decoding in Brain-computer Interfaces—0
Banyan: Improved Representation Learning with Explicit Structure—0
X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs—0
Shapley Value-based Contrastive Alignment for Multimodal Information Extraction—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction—0
Distribution-Aware Robust Learning from Long-Tailed Data with Noisy LabelsCode0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation—0
Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring—0
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift—0
A Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation—0
Balanced Multi-Relational Graph ClusteringCode0
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
NV-Retriever: Improving text embedding models with effective hard-negative mining—0
Weak-to-Strong Compositional Learning from Generative Models for Language-based Object Detection—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Self-supervised transformer-based pre-training method with General Plant Infection datasetCode0
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual AlignmentCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis—0
L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative FilteringCode0
Improving classification of road surface conditions via road area extraction and contrastive learning—0
Contrastive Learning with Counterfactual Explanations for Radiology Report Generation—0
Advancing Melanoma Diagnosis with Self-Supervised Neural Networks: Evaluating the Effectiveness of Different Techniques—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