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

Showing 23262350 of 10307 papers

TitleStatusHype
Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting0
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch0
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data0
Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning0
A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem0
Deep Learning based Automated Forest Health Diagnosis from Aerial Images0
Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds0
Boosting Self-Supervised Learning via Knowledge Transfer0
An exploratory experiment on Hindi, Bengali hate-speech detection and transfer learning using neural networks0
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer0
Boosting pathology detection in infants by deep transfer learning from adult speech0
Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning0
Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning0
Deep Learning-based Extreme Heatwave Forecast0
Boosting offline handwritten text recognition in historical documents with few labeled lines0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations0
An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection0
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies0
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning0
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding0
A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring0
An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm0
Boosting HDR Image Reconstruction via Semantic Knowledge Transfer0
A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)0
An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified