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 451–475 of 6661 papers

TitleStatusHype
External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection—0
Structural Entropy Guided Unsupervised Graph Out-Of-Distribution DetectionCode0
Intermediate Domain-guided Adaptation for Unsupervised Chorioallantoic Membrane Vessel SegmentationCode0
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning—0
Unsupervised Waste Classification By Dual-Encoder Contrastive Learning and Multi-Clustering Voting (DECMCV)—0
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning—0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification—0
OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels—0
OFF-CLIP: Improving Normal Detection Confidence in Radiology CLIP with Simple Off-Diagonal Term Auto-AdjustmentCode0
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation AlignmentCode0
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering—0
Learning Actionable World Models for Industrial Process Control—0
Random Walks in Self-supervised Learning for Triangular Meshes—0
Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive LearningCode1
Convergence of energy-based learning in linear resistive networks—0
Projection Head is Secretly an Information BottleneckCode0
BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds—0
Discovering Global False Negatives On the Fly for Self-supervised Contrastive LearningCode0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
UoR-NCL at SemEval-2025 Task 1: Using Generative LLMs and CLIP Models for Multilingual Multimodal Idiomaticity RepresentationCode0
Subtask-Aware Visual Reward Learning from Segmented Demonstrations—0
cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning—0
Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification—0
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion—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