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 2101–2125 of 6661 papers

TitleStatusHype
What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent PerceptionCode0
Multiscale Matching Driven by Cross-Modal Similarity Consistency for Audio-Text Retrieval—0
Improving Medical Multi-modal Contrastive Learning with Expert AnnotationsCode0
Computer User Interface Understanding. A New Dataset and a Learning Framework—0
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive LearningCode1
Unsupervised Modality-Transferable Video Highlight Detection with Representation Activation Sequence Learning—0
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality—0
Counterfactual contrastive learning: robust representations via causal image synthesisCode1
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D UnderstandingCode1
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic GraspingCode2
Anatomical Structure-Guided Medical Vision-Language Pre-training—0
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization—0
Hyper-CL: Conditioning Sentence Representations with HypernetworksCode1
Dyadic Interaction Modeling for Social Behavior GenerationCode1
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow—0
Deep Submodular Peripteral Networks—0
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?—0
MolBind: Multimodal Alignment of Language, Molecules, and Proteins—0
Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)—0
Self-supervised Contrastive Learning for Implicit Collaborative Filtering—0
Disentangling Policy from Offline Task Representation Learning via Adversarial Data AugmentationCode0
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning—0
A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing ModelsCode0
Intra-video Positive Pairs in Self-Supervised Learning for UltrasoundCode0
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without AnnotationsCode0
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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