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 1901–1925 of 6661 papers

TitleStatusHype
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based RecommendationCode0
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach—0
Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target DetectionCode0
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation—0
Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender SystemCode0
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition—0
DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation—0
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud RecognitionCode0
Detection of Breast Cancer Lumpectomy Margin with SAM-incorporated Forward-Forward Contrastive LearningCode0
FedSC: Federated Learning with Semantic-Aware CollaborationCode0
AgentStealth: Reinforcing Large Language Model for Anonymizing User-generated TextCode0
V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling—0
Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous GraphsCode0
Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer FeaturesCode0
PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching—0
Directed Link Prediction using GNN with Local and Global Feature Fusion—0
scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection—0
ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations—0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT—0
Iterative Quantum Feature Maps—0
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECGCode0
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning—0
VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT RegistrationCode0
SSAVSV: Towards Unified Model for Self-Supervised Audio-Visual Speaker Verification—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