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 2451–2475 of 10307 papers

TitleStatusHype
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency—0
An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images—0
A Deep Learning Framework for Lifelong Machine Learning—0
Biasing & Debiasing based Approach Towards Fair Knowledge Transfer for Equitable Skin Analysis—0
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media—0
An Ensemble Approach to Personalized Real Time Predictive Writing for Experts—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond—0
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation—0
A Survey on Curriculum Learning—0
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation—0
Beyond Transfer Learning: Co-finetuning for Action Localisation—0
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications—0
Differentially Private Prototypes for Imbalanced Transfer Learning—0
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection—0
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling—0
Beyond Similarity: A Gradient-based Graph Method for Instruction Tuning Data Selection—0
An End-to-End Attack on Text-based CAPTCHAs Based on Cycle-Consistent Generative Adversarial Network—0
A Deep Learning-Based GPR Forward Solver for Predicting B-Scans of Subsurface Objects—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO—0
Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review—0
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer—0
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration—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