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 4901–4925 of 10307 papers

TitleStatusHype
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference—0
Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?—0
Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time—0
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
Robustness via Deep Low-Rank Representations—0
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets—0
Introducing the structural bases of typicality effects in deep learning—0
Introspective Action Advising for Interpretable Transfer Learning—0
Your representations are in the network: composable and parallel adaptation for large scale models—0
Inverse Density as an Inverse Problem: The Fredholm Equation Approach—0
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning—0
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil—0
Inverse design with conditional cascaded diffusion models—0
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics—0
Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
Investigating Continuous Learning in Spiking Neural Networks—0
Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model—0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem—0
Investigating Multilingual NMT Representations at Scale—0
Investigating Relative Performance of Transfer and Meta Learning—0
Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Bangla—0
Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications—0
Investigating the Impact of Weight Sharing Decisions on Knowledge Transfer in Continual Learning—0
Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation—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