SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 6176–6200 of 10307 papers

TitleStatusHype
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems—0
Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks—0
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy—0
Private Knowledge Transfer via Model Distillation with Generative Adversarial Networks—0
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels—0
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation—0
Proactive Guidance of Multi-Turn Conversation in Industrial Search—0
Balanced Distribution Adaptation for Transfer Learning—0
Probabilistic Meta-Learning for Bayesian Optimization—0
Probabilistic Models of Cross-Lingual Semantic Similarity in Context Based on Latent Cross-Lingual Concepts Induced from Comparable Data—0
Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring—0
Probabilistic Neural Network with Complex Exponential Activation Functions in Image Recognition using Deep Learning Framework—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Probabilistic Reasoning via Deep Learning: Neural Association Models—0
Probabilistic Self-supervised Learning via Scoring Rules Minimization—0
Probabilistic transfer learning methodology to expedite high fidelity simulation of reactive flows—0
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning—0
Probing Transfer in Deep Reinforcement Learning without Task Engineering—0
Probing transfer learning with a model of synthetic correlated datasets—0
Probing TryOnGAN—0
Problems in AI research and how the SP System may help to solve them—0
Procedural Content Generation via Knowledge Transformation (PCG-KT)—0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning—0
Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models—0
Programmable metasurfaces for future photonic artificial intelligence—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified