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 7651–7675 of 10307 papers

TitleStatusHype
Model-Agnostic Private Learning—0
Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning—0
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer—0
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems—0
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey—0
Model-based Large Language Model Customization as Service—0
Model-based Reinforcement Learning: A Survey—0
Amortized Network Intervention to Steer the Excitatory Point Processes—0
Task Decomposition for Iterative Learning Model Predictive Control—0
The Changing Role of Entrepreneurial Universities in the Altering Innovation Policy: Opportunities Arising from the Paradigm Change in Light of the Experience of Széchenyi István University—0
Adaptive Sample Aggregation In Transfer Learning—0
Model Bias in NLP -- Application to Hate Speech Classification using transfer learning techniques—0
Model-Contrastive Federated Domain Adaptation—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Model Diffusion for Certifiable Few-shot Transfer Learning—0
Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification—0
Model-Driven Beamforming Neural Networks—0
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning—0
Model Evaluation for Domain Identification of Unknown Classes in Open-World Recognition: A Proposal—0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
Source-Free Cross-Modal Knowledge Transfer by Unleashing the Potential of Task-Irrelevant Data—0
Source-Free Domain Adaptation for Semantic Segmentation—0
Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects—0
Source-free Domain Adaptation Requires Penalized Diversity—0
Modeling Information Flow Through Deep Neural Networks—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