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 4051–4100 of 6661 papers

TitleStatusHype
Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images—0
Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning—0
Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection—0
Label-free Prediction of Vascular Connectivity in Perfused Microvascular Networks in vitro—0
Label-invariant Augmentation for Semi-Supervised Graph Classification—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
LabelPrompt: Effective Prompt-based Learning for Relation Classification—0
Label-template based Few-Shot Text Classification with Contrastive Learning—0
LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space—0
LAC: Latent Action Composition for Skeleton-based Action Segmentation—0
LAC - Latent Action Composition for Skeleton-based Action Segmentation—0
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs—0
Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning—0
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification—0
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning—0
Language-guided Image Reflection Separation—0
Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving—0
Large Language Model-Aware In-Context Learning for Code Generation—0
Large Language Model Meets Graph Neural Network in Knowledge Distillation—0
Large Language Models Enhanced Hyperbolic Space Recommender Systems—0
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping—0
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning—0
LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding—0
Latent Prompt Tuning for Text Summarization—0
Latent Space Consistency for Sparse-View CT Reconstruction—0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning—0
Neighbour Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification—0
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis—0
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties—0
NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text—0
NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks—0
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models—0
Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning—0
Neuro-Symbolic Contrastive Learning for Cross-domain Inference—0
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training—0
NewsEmbed: Modeling News through Pre-trained Document Representations—0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
Night-to-Day Translation via Illumination Degradation Disentanglement—0
NijiGAN: Transform What You See into Anime with Contrastive Semi-Supervised Learning and Neural Ordinary Differential Equations—0
NJUST-KMG at TRAC-2024 Tasks 1 and 2: Offline Harm Potential Identification—0
Node Embeddings via Neighbor Embeddings—0
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task—0
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models—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