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 3801–3825 of 10307 papers

TitleStatusHype
Learning from What is Already Out There: Few-shot Sign Language Recognition with Online DictionariesCode0
MOTOR: A Time-To-Event Foundation Model For Structured Medical RecordsCode1
Transfer learning for conflict and duplicate detection in software requirement pairsCode0
Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in FinanceCode0
Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning—0
Energy Disaggregation & Appliance Identification in a Smart Home: Transfer Learning enables Edge Computing—0
Causal Categorization of Mental Health Posts using Transformers—0
LS-DYNA Machine Learning-based Multiscale Method for Nonlinear Modeling of Short Fiber-Reinforced Composites—0
SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced TrainingCode0
Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural networkCode0
Event Camera Data Pre-training—0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis—0
ANNA: Abstractive Text-to-Image Synthesis with Filtered News CaptionsCode0
A Survey on Deep Industrial Transfer Learning in Fault Prognostics—0
Transfer Learning for Classification of Alzheimer's Disease Based on Genome Wide Data—0
Finding the Most Transferable Tasks for Brain Image Segmentation—0
Language Models are Drummers: Drum Composition with Natural Language Pre-TrainingCode1
Transfer Generative Adversarial Networks (T-GAN)-based Terahertz Channel Modeling—0
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)—0
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction—0
Transferable Energy Storage Bidder—0
CLIP-Driven Universal Model for Organ Segmentation and Tumor DetectionCode2
Improved Training for 3D Point Cloud ClassificationCode0
Computation and Data Efficient Backdoor Attacks—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