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 2926–2950 of 6661 papers

TitleStatusHype
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-TrainingCode0
Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment—0
Vision-Language Models Assisted Unsupervised Video Anomaly Detection—0
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models—0
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance—0
Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive LearningCode0
ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning—0
High-dimensional learning of narrow neural networks—0
Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning—0
RingMo-Aerial: An Aerial Remote Sensing Foundation Model With A Affine Transformation Contrastive Learning—0
Recent Advancement of Emotion Cognition in Large Language Models—0
COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image UnderstandingCode0
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification—0
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning—0
Multi-View Adaptive Contrastive Learning for Information Retrieval Based Fault Localization—0
Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving—0
RUIE: Retrieval-based Unified Information Extraction using Large Language ModelCode0
Multimodal Generalized Category Discovery—0
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation—0
JEAN: Joint Expression and Audio-guided NeRF-based Talking Face Generation—0
CLIP Adaptation by Intra-modal Overlap Reduction—0
Contrastive Learning in Memristor-based Neuromorphic Systems—0
Learning Spatially-Aware Language and Audio Embeddings—0
Self-Contrastive Forward-Forward Algorithm—0
Fair Anomaly Detection For Imbalanced Groups—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