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 801–825 of 10307 papers

TitleStatusHype
A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring—0
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning—0
One for Dozens: Adaptive REcommendation for All Domains with Counterfactual AugmentationCode0
Multilabel Classification for Lung Disease Detection: Integrating Deep Learning and Natural Language Processing—0
Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters ThemselvesCode1
Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain AdaptationCode0
Relation-Guided Adversarial Learning for Data-free Knowledge TransferCode1
Fast-staged CNN Model for Accurate pulmonary diseases and Lung cancer detection—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data—0
TinySubNets: An efficient and low capacity continual learning strategyCode0
Deep Learning Models for Colloidal Nanocrystal SynthesisCode0
Linked Adapters: Linking Past and Future to Present for Effective Continual Learning—0
Global Estimation of Subsurface Eddy Kinetic Energy of Mesoscale Eddies Using a Multiple-input Residual Neural Network—0
Data-Driven Transfer Learning Framework for Estimating Turning Movement Counts—0
All-in-One: Transferring Vision Foundation Models into Stereo Matching—0
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning—0
BayesAdapter: enhanced uncertainty estimation in CLIP few-shot adaptation—0
Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy—0
Transfer Learning of RSSI to Improve Indoor Localisation PerformanceCode0
A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal AdapterCode0
Evaluating Pixel Language Models on Non-Standardized Languages—0
Improvement in Sign Language Translation Using Text CTC AlignmentCode0
A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis—0
AFFAKT: A Hierarchical Optimal Transport based Method for Affective Facial Knowledge Transfer in Video Deception Detection—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