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 8826–8850 of 10307 papers

TitleStatusHype
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier—0
Energy Predictive Models with Limited Data using Transfer Learning—0
Prediction of Workplace Injuries—0
A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging—0
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications—0
Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition—0
Transfer Learning with intelligent training data selection for prediction of Alzheimer's DiseaseCode0
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid—0
An Adaptive Random Path Selection Approach for Incremental LearningCode0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset—0
Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation—0
Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection—0
It's Not About the Journey; It's About the Destination: Following Soft Paths Under Question-Guidance for Visual Reasoning—0
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Large-Scale Few-Shot Learning: Knowledge Transfer With Class HierarchyCode0
Cross-lingual Transfer Learning for Japanese Named Entity Recognition—0
Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning ApproachesCode0
MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection—0
Min-Max Statistical Alignment for Transfer Learning—0
NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers—0
Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection—0
Language Discrimination and Transfer Learning for Similar Languages: Experiments with Feature Combinations and Adaptation—0
LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification—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