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

Showing 226250 of 6661 papers

TitleStatusHype
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines0
Rethinking Graph Contrastive Learning through Relative Similarity Preservation0
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition0
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings0
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights0
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution0
STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis0
Path and Bone-Contour Regularized Unpaired MRI-to-CT TranslationCode0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition0
JTCSE: Joint Tensor-Modulus Constraints and Cross-Attention for Unsupervised Contrastive Learning of Sentence EmbeddingsCode0
VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge InjectionCode0
ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance ApplicationsCode1
Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments0
Weakly-supervised Audio Temporal Forgery Localization via Progressive Audio-language Co-learning NetworkCode0
An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding0
SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End DevicesCode0
OmicsCL: Unsupervised Contrastive Learning for Cancer Subtype Discovery and Survival StratificationCode0
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass0
Recursive KL Divergence Optimization: A Dynamic Framework for Representation LearningCode1
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects0
ISDrama: Immersive Spatial Drama Generation through Multimodal Prompting0
VCM: Vision Concept Modeling Based on Implicit Contrastive Learning with Vision-Language Instruction Fine-Tuning0
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding0
ClearVision: Leveraging CycleGAN and SigLIP-2 for Robust All-Weather Classification in Traffic Camera Imagery0
Show:102550
← PrevPage 10 of 267Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified