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 3101–3125 of 6661 papers

TitleStatusHype
Contrastive Learning for Cross-modal Artist Retrieval—0
BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation—0
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach—0
CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services—0
Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment LocalizationCode0
AD-CLIP: Adapting Domains in Prompt Space Using CLIPCode1
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imagingCode1
Induction Network: Audio-Visual Modality Gap-Bridging for Self-Supervised Sound Source LocalizationCode0
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive LearningCode0
Cross-view Semantic Alignment for Livestreaming Product RecognitionCode0
Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognition—0
D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance AnnotationCode1
Exploring Transformers for Open-world Instance Segmentation—0
Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation LearningCode1
Expression Prompt Collaboration Transformer for Universal Referring Video Object SegmentationCode0
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition—0
A Hybrid CNN-Transformer Architecture with Frequency Domain Contrastive Learning for Image Deraining—0
SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon PredictionCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
Local Structure-aware Graph Contrastive Representation Learning—0
Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation—0
Towards General Text Embeddings with Multi-stage Contrastive Learning—0
Multi-Label Self-Supervised Learning with Scene Images—0
Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methods—0
Source-free Domain Adaptive Human Pose EstimationCode1
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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