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 2201–2225 of 6661 papers

TitleStatusHype
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI—0
Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations—0
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning—0
On the Perception Bottleneck of VLMs for Chart UnderstandingCode0
Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning—0
OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning—0
Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative SamplingCode0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images—0
Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning—0
NaFM: Pre-training a Foundation Model for Small-Molecule Natural ProductsCode0
Should we pre-train a decoder in contrastive learning for dense prediction tasks?—0
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics—0
Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural NetworksCode0
Neural-Guided Equation DiscoveryCode0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Generative Modeling of Class Probability for Multi-Modal Representation Learning—0
DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering—0
Diffusion-augmented Graph Contrastive Learning for Collaborative Filter—0
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation—0
UniSync: A Unified Framework for Audio-Visual Synchronization—0
GAIR: Improving Multimodal Geo-Foundation Model with Geo-Aligned Implicit Representations—0
PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing RetrievalCode0
Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image ClassificationCode0
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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