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 2276–2300 of 6661 papers

TitleStatusHype
Intermediate Domain-guided Adaptation for Unsupervised Chorioallantoic Membrane Vessel SegmentationCode0
Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context ScenariosCode0
Variance-Aware Loss Scheduling for Multimodal Alignment in Low-Data Settings—0
External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection—0
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning—0
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning—0
Unsupervised Waste Classification By Dual-Encoder Contrastive Learning and Multi-Clustering Voting (DECMCV)—0
OFF-CLIP: Improving Normal Detection Confidence in Radiology CLIP with Simple Off-Diagonal Term Auto-AdjustmentCode0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering—0
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification—0
Learning Actionable World Models for Industrial Process Control—0
OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels—0
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation AlignmentCode0
Random Walks in Self-supervised Learning for Triangular Meshes—0
Projection Head is Secretly an Information BottleneckCode0
BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds—0
Convergence of energy-based learning in linear resistive networks—0
Discovering Global False Negatives On the Fly for Self-supervised Contrastive LearningCode0
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
Continuous Adversarial Text Representation Learning for Affective Recognition—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