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 5476–5500 of 6661 papers

TitleStatusHype
Learning Moving-Object Tracking with FMCW LiDAR—0
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Learning Music Sequence Representation from Text Supervision—0
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER—0
Learning on Graphs under Label Noise—0
Learning Referring Video Object Segmentation from Weak Annotation—0
Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification—0
Learning Representation for Anomaly Detection of Vehicle Trajectories—0
Learning Representations by Contrasting Clusters While Bootstrapping Instances—0
Learning Representations for Pixel-based Control: What Matters and Why?—0
Learning Reward Functions for Robotic Manipulation by Observing Humans—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
Learning Self-Supervised Audio-Visual Representations for Sound Recommendations—0
Learning Sound Localization Better From Semantically Similar Samples—0
Learning Spatially-Aware Language and Audio Embeddings—0
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation—0
Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application—0
Learning Temporal Invariance in Android Malware Detectors—0
Learning the Visualness of Text Using Large Vision-Language Models—0
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting—0
Learning to Anticipate Egocentric Actions by Imagination—0
Learning Multi-Agent Communication with Contrastive Learning—0
Learning to Ground Decentralized Multi-Agent Communication with Contrastive Learning—0
Learning To Hallucinate Examples From Extrinsic and Intrinsic Supervision—0
Learning to Hash Naturally Sorts—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