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 7901–7925 of 10307 papers

TitleStatusHype
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches—0
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures—0
Using GPT-2 to Create Synthetic Data to Improve the Prediction Performance of NLP Machine Learning Classification Models—0
Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets—0
Using Human-like Mechanism to Weaken Effect of Pre-training Weight Bias in Face-Recognition Convolutional Neural Network—0
Using Human Perception to Regularize Transfer Learning—0
Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement—0
Using i-vectors for subject-independent cross-session EEG transfer learning—0
Using Large Language Models to Generate Clinical Trial Tables and Figures—0
Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning—0
Using Mobile Data and Deep Models to Assess Auditory Verbal Hallucinations—0
Using Multi-task and Transfer Learning to Solve Working Memory Tasks—0
Using Neural Transfer Learning for Morpho-syntactic Tagging of South-Slavic Languages Tweets—0
Exploring the parameter reusability of CNN—0
Using Unlabeled Data for Increasing Low-Shot Classification Accuracy of Relevant and Open-Set Irrelevant Images—0
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer—0
Using The Concept Hierarchy for Household Action Recognition—0
Using Transfer Learning for Image-Based Cassava Disease Detection—0
Using Transfer Learning to Assist Exploratory Corpus Annotation—0
Using Transfer Learning to Automatically Mark L2 Writing Texts—0
Using transfer learning to detect galaxy mergers—0
Using transfer learning to study burned area dynamics: A case study of refugee settlements in West Nile, Northern Uganda—0
Using UNet and PSPNet to explore the reusability principle of CNN parameters—0
Utilisation of open intent recognition models for customer support intent detection—0
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning—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