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 2701–2725 of 6661 papers

TitleStatusHype
Adversarial Contrastive Estimation—0
Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis—0
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification—0
Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation—0
Contrastive masked auto-encoders based self-supervised hashing for 2D image and 3D point cloud cross-modal retrieval—0
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images—0
StackMix: A complementary Mix algorithm—0
Contrastively Enforcing Distinctiveness for Multi-Label Classification—0
Image Retrieval with Intra-Sweep Representation Learning for Neck Ultrasound Scanning Guidance—0
BRIDO: Bringing Democratic Order to Abstractive Summarization—0
Bridging the Modality Gap: Dimension Information Alignment and Sparse Spatial Constraint for Image-Text Matching—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Contrastive Learning with Positive-Negative Frame Mask for Music Representation—0
Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)—0
Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning—0
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation—0
Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning—0
Contrastive Learning with Negative Sampling Correction—0
Contrastive Learning with Nasty Noise—0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
Bridging the Emotional Semantic Gap via Multimodal Relevance Estimation—0
Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps—0
Bridging Text and Image for Artist Style Transfer via Contrastive Learning—0
Anomalies, Representations, and Self-Supervision—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