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 7951–7975 of 10307 papers

TitleStatusHype
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling—0
A review of sentiment analysis research in Arabic language—0
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation—0
Domain Specific, Semi-Supervised Transfer Learning for Medical Imaging—0
Automatic Discovery of Novel Intents & Domains from Text Utterances—0
A machine learning approach to using Quality-of-Life patient scores in guiding prostate radiation therapy dosing—0
Bridging the gap between Natural and Medical Images through Deep ColorizationCode0
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning—0
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation—0
Classification of Industrial Control Systems screenshots using Transfer Learning—0
Hidden Markov Models and their Application for Predicting Failure Events—0
Self-supervised Transfer Learning for Instance Segmentation through Physical InteractionCode0
Cross-lingual Approaches for Task-specific Dialogue Act Recognition—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
Transfer learning based multi-fidelity physics informed deep neural network—0
Information-theoretic analysis for transfer learning—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer—0
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformationsCode0
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation—0
KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT—0
Neural Entity Linking on Technical Service Tickets—0
Industrial Federated Learning -- Requirements and System Design—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