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 6726–6750 of 10307 papers

TitleStatusHype
Knowledge distillation with error-correcting transfer learning for wind power prediction—0
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community—0
Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification—0
Knowledge Efficient Deep Learning for Natural Language Processing—0
Sequential Transfer Learning to Decode Heard and Imagined Timbre from fMRI Data—0
EA-KD: Entropy-based Adaptive Knowledge Distillation—0
Text-Aware Adapter for Few-Shot Keyword Spotting—0
Knowledge Graph Embeddings for Multi-Lingual Structured Representations of Radiology Reports—0
Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving—0
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions—0
1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems—0
A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network—0
Knowledge-Guided Wasserstein Distributionally Robust Optimization—0
Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing—0
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations—0
Knowledge Inheritance for Pre-trained Language Models—0
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer—0
Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better—0
Knowledge Management for Automobile Failure Analysis Using Graph RAG—0
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift—0
Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science—0
Knowledge Squeezed Adversarial Network Compression—0
Sexism Identification in Tweets and Gabs using Deep Neural Networks—0
Knowledge Transfer Across Modalities with Natural Language Supervision—0
Knowledge Transfer across Multiple Principal Component Analysis Studies—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