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 3776–3800 of 10307 papers

TitleStatusHype
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case StudyCode0
Ensemble Transfer Learning for Multilingual Coreference Resolution—0
Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese HokkienCode0
Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection—0
Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance—0
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research—0
MV-Adapter: Multimodal Video Transfer Learning for Video Text RetrievalCode1
Vision Learners Meet Web Image-Text Pairs—0
Transformers as Algorithms: Generalization and Stability in In-context LearningCode0
Prompting Large Language Model for Machine Translation: A Case Study—0
Vision Based Machine Learning Algorithms for Out-of-Distribution Generalisation—0
Towards Estimating Transferability using Hard Subsets—0
Multicenter automatic detection of invasive carcinoma on breast whole slide images—0
Monocular Cyclist Detection with Convolutional Neural Networks—0
Multimodal Side-Tuning for Document ClassificationCode1
Model-based Transfer Learning for Automatic Optical Inspection based on domain discrepancyCode0
Discovery of 2D materials using Transformer Network based Generative DesignCode2
FUN with Fisher: Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled UnfreezingCode0
Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival dataCode0
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks—0
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly—0
Language-Informed Transfer Learning for Embodied Household Activities—0
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning—0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—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