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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7526–7550 of 10307 papers

TitleStatusHype
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization—0
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers—0
Meta-Learning of Neural State-Space Models Using Data From Similar Systems—0
A CNN-based approach to classify cricket bowlers based on their bowling actions—0
Meta-learning Transferable Representations with a Single Target Domain—0
The ART of Transfer Learning: An Adaptive and Robust Pipeline—0
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization—0
MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling—0
Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis—0
Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning—0
Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement—0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
Meta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal—0
Analysis of Multilingual Sequence-to-Sequence speech recognition systems—0
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis—0
Meta Transfer Learning for Emotion Recognition—0
Meta Transfer Learning for Facial Emotion Recognition—0
An Analysis of Semantically-Aligned Speech-Text Embeddings—0
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers—0
Analysis and Prediction of NLP models via Task Embeddings—0
Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images—0
MetaTune: Meta-Learning Based Cost Model for Fast and Efficient Auto-tuning Frameworks—0
Meta Variance Transfer: Learning to Augment from the Others—0
MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning—0
An AI-driven framework for the prediction of personalised health response to air pollution—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