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 2851–2875 of 6661 papers

TitleStatusHype
ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation—0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
Contrastive Learning for Image Complexity Representation—0
Hallucination Improves the Performance of Unsupervised Visual Representation Learning—0
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation—0
HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals—0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—0
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency—0
Inductive-Biases for Contrastive Learning of Disentangled Representations—0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis—0
Generative Modeling of Class Probability for Multi-Modal Representation Learning—0
Contrastive Learning of Global and Local Video Representations—0
Contrastive Multi-Task Dense Prediction—0
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Hashing based Contrastive Learning for Virtual Screening—0
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise—0
HateSieve: A Contrastive Learning Framework for Detecting and Segmenting Hateful Content in Multimodal Memes—0
Hate Speech Detection via Dual Contrastive Learning—0
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation—0
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction—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