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 976–1000 of 6661 papers

TitleStatusHype
Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation ModelsCode0
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation—0
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking—0
Variational Graph Contrastive LearningCode0
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering—0
KMM: Key Frame Mask Mamba for Extended Motion GenerationCode1
PRISM: Privacy-preserving Inter-Site MRI Harmonization via Disentangled Representation LearningCode0
Understanding the Role of Equivariance in Self-supervised LearningCode0
Multimodal Contrastive Learning of Urban Space Representations from POI DataCode1
GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly DetectionCode1
Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns—0
Reducing Distraction in Long-Context Language Models by Focused Learning—0
Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised LearningCode0
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised LearningCode0
ImpScore: A Learnable Metric For Quantifying The Implicitness Level of LanguageCode0
LLM2CLIP: Powerful Language Model Unlocks Richer Visual RepresentationCode4
ACCIO: Table Understanding Enhanced via Contrastive Learning with AggregationsCode0
Efficient Fourier Filtering Network with Contrastive Learning for UAV-based Unaligned Bi-modal Salient Object DetectionCode1
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing—0
Understanding Contrastive Learning via Gaussian Mixture Models—0
On the Comparison between Multi-modal and Single-modal Contrastive Learning—0
Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification—0
Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval—0
Fine Grained Insider Risk Detection—0
Learning General-Purpose Biomedical Volume Representations using Randomized SynthesisCode2
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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