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 3826–3850 of 6661 papers

TitleStatusHype
Deep Clustering with Diffused Sampling and Hardness-aware Self-distillationCode0
Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery—0
Enhancing cross-domain detection: adaptive class-aware contrastive transformer—0
Towards Efficient and Effective Deep Clustering with Dynamic Grouping and Prototype AggregationCode0
Learning Representations for Clustering via Partial Information Discrimination and Cross-Level InteractionCode0
Consistency Enhancement-Based Deep Multiview Clustering via Contrastive Learning—0
Contrastive Learning in Distilled ModelsCode0
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing—0
Joint Audio-Visual Attention with Contrastive Learning for More General Deepfake Detection—0
Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation—0
SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning—0
Self-supervised Contrastive Learning for 6G UM-MIMO THz Communications: Improving Robustness Under Imperfect CSI—0
PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide EncodingCode0
Visual Imitation Learning with Calibrated Contrastive Representation—0
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection—0
Adversarial Robustness of Link Sign Prediction in Signed Graphs—0
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction PredictionCode0
Named Entity Recognition Under Domain Shift via Metric Learning for Life SciencesCode0
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition—0
Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning—0
Enhancing medical vision-language contrastive learning via inter-matching relation modelling—0
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization—0
EfficientRec an unlimited user-item scale recommendation system based on clustering and users interaction embedding profileCode0
CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Re-IdentificationCode0
Self-supervised New Activity Detection in Sensor-based Smart Environments—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