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 4601–4625 of 6661 papers

TitleStatusHype
SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives—0
Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID—0
Registering Neural Radiance Fields as 3D Density Images—0
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine TranslationCode0
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification—0
From Patches to Objects: Exploiting Spatial Reasoning for Better Visual Representations—0
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model—0
Low-Entropy Latent Variables Hurt Out-of-Distribution Performance—0
Mitigating Catastrophic Forgetting in Task-Incremental Continual Learning with Adaptive Classification Criterion—0
Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection—0
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark ResultsCode0
Incomplete Multi-view Clustering via Diffusion Completion—0
Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method—0
A Topic-aware Summarization Framework with Different Modal Side Information—0
Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity—0
Quantifying stimulus-relevant representational drift using cross-modality contrastive learning—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
Tuned Contrastive Learning—0
Speech Separation based on Contrastive Learning and Deep Modularization—0
From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion RetrievalCode0
How does Contrastive Learning Organize Images?Code0
Sharpness & Shift-Aware Self-Supervised Learning—0
HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph EmbeddingCode0
TG-VQA: Ternary Game of Video Question Answering—0
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization—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