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 5326–5350 of 6661 papers

TitleStatusHype
Invariance-adapted decomposition and Lasso-type contrastive learning—0
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition—0
InverTune: Removing Backdoors from Multimodal Contrastive Learning Models via Trigger Inversion and Activation Tuning—0
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis—0
Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition—0
Investigating Graph Structure Information for Entity Alignment with Dangling Cases—0
End-to-End Lyrics Recognition with Self-supervised Learning—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
Investigating the Benefits of Projection Head for Representation Learning—0
Investigating the Role of Negatives in Contrastive Representation Learning—0
Investigating Why Contrastive Learning Benefits Robustness Against Label Noise—0
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain—0
Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text—0
Is Cross-modal Information Retrieval Possible without Training?—0
"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision—0
ISDrama: Immersive Spatial Drama Generation through Multimodal Prompting—0
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space—0
Isolating authorship from content with semantic embeddings and contrastive learning—0
I Speak and You Find: Robust 3D Visual Grounding with Noisy and Ambiguous Speech Inputs—0
Is Self-Supervised Learning More Robust Than Supervised Learning?—0
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning—0
Iterated Learning Improves Compositionality in Large Vision-Language Models—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
Iterative Graph Self-Distillation—0
Iterative Quantum Feature Maps—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