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 3726–3750 of 10307 papers

TitleStatusHype
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Explicit Connection Distillation—0
Explicit Domain Adaptation with Loosely Coupled Samples—0
Experiments on transfer learning architectures for biomedical relation extraction—0
Experimental and causal view on information integration in autonomous agents—0
Experience Selection Using Dynamics Similarity for Efficient Multi-Source Transfer Learning Between Robots—0
Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?—0
ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)—0
CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures—0
Active Sentiment Domain Adaptation—0
Exploiting Convolution Filter Patterns for Transfer Learning—0
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems—0
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation—0
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Experience Reuse with Probabilistic Movement Primitives—0
Exploiting Social Tags for Cross-Domain Collaborative Filtering—0
Expectation maximization transfer learning and its application for bionic hand prostheses—0
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning—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