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 4701–4725 of 10307 papers

TitleStatusHype
On-ramp and Off-ramp Traffic Flows Estimation Based on A Data-driven Transfer Learning Framework—0
On Reinforcement Learning for Full-length Game of StarCraft—0
On Romanization for Model Transfer Between Scripts in Neural Machine Translation—0
On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach—0
On Target Representation in Continuous-output Neural Machine Translation—0
On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains—0
On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing—0
On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics—0
On the application of transfer learning in prognostics and health management—0
On the Behavior of Convolutional Nets for Feature Extraction—0
On the comparability of Pre-trained Language Models—0
On the Condition Monitoring of Bolted Joints through Acoustic Emission and Deep Transfer Learning: Generalization, Ordinal Loss and Super-Convergence—0
On the cross-lingual transferability of multilingual prototypical models across NLU tasks—0
On the definition of a general learning system with user-defined operators—0
On the Design of Communication-Efficient Federated Learning for Health Monitoring—0
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data—0
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness—0
A Deep Transfer Learning Approach on Identifying Glitch Wave-form in Gravitational Wave Data—0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning—0
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings—0
On the Generalization Gap in Reparameterizable Reinforcement Learning—0
On the Generalization of Handwritten Text Recognition Models—0
On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets—0
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study—0
On the impact of incorporating task-information in learning-based image denoising—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