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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2651–2675 of 6661 papers

TitleStatusHype
Graph Contrastive Learning with Cross-view Reconstruction—0
A Closer Look at Few-shot Image Generation—0
Contrastive Rendering for Ultrasound Image Segmentation—0
Contrastive random lead coding for channel-agnostic self-supervision of biosignals—0
Contrastive Quant: Quantization Makes Stronger Contrastive Learning—0
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing—0
Anti-Compression Contrastive Facial Forgery Detection—0
HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning—0
Contrastive Prompt Learning-based Code Search based on Interaction Matrix—0
C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning—0
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography—0
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models—0
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding—0
Contrastive Pre-training for Zero-Shot Information Retrieval—0
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs—0
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification—0
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation—0
Contrastive Pre-training for Deep Session Data Understanding—0
Contrastive Predictive Coding for Anomaly Detection—0
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning—0
C^3: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues—0
Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Models—0
Contrastive News and Social Media Linking using BERT for Articles and Tweets across Dual Platforms—0
C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network—0
A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion—0
Show:102550
← PrevPage 107 of 267Next →
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