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 4701–4725 of 6661 papers

TitleStatusHype
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange—0
Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions—0
Embodied vision for learning object representations—0
Embracing Unimodal Aleatoric Uncertainty for Robust Multimodal Fusion—0
eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking—0
Emotion-Guided Image to Music Generation—0
EmotionRankCLAP: Bridging Natural Language Speaking Styles and Ordinal Speech Emotion via Rank-N-Contrast—0
Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning—0
Emotions are Subtle: Learning Sentiment Based Text Representations Using Contrastive Learning—0
EMP: Enhance Memory in Data Pruning—0
Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities—0
Enabling Efficient On-Device Self-supervised Contrastive Learning by Data Selection—0
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast—0
EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning—0
Encoding Binary Events from Continuous Time Series in Rooted Trees using Contrastive Learning—0
Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records—0
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis—0
End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction—0
End-to-end multi-modal product matching in fashion e-commerce—0
End-to-end Semantic-centric Video-based Multimodal Affective Computing—0
End-to-end Semantic Object Detection with Cross-Modal Alignment—0
Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning—0
Enhanced Multimodal Representation Learning with Cross-modal KD—0
Enhanced Soft Label for Semi-Supervised Semantic Segmentation—0
Enhanced Unsupervised Image-to-Image Translation Using Contrastive Learning and Histogram of Oriented Gradients—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