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 6801–6825 of 10307 papers

TitleStatusHype
KTCR: Improving Implicit Hate Detection with Knowledge Transfer driven Concept Refinement—0
Shape-aware Generative Adversarial Networks for Attribute Transfer—0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme—0
A Novel DNN Training Framework via Data Sampling and Multi-Task Optimization—0
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models—0
Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognition—0
Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition—0
Detection of Myocardial Infarction Based on Novel Deep Transfer Learning Methods for Urban Healthcare in Smart Cities—0
Labeled Data Selection for Category Discovery—0
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions—0
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks—0
Label Efficient Learning of Transferable Representations across Domains and Tasks—0
A Novel Deep Learning Method for Textual Sentiment Analysis—0
Label-efficient Time Series Representation Learning: A Review—0
A novel database of Children's Spontaneous Facial Expressions (LIRIS-CSE)—0
A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detection—0
Label Representations in Modeling Classification as Text Generation—0
LABOR-LLM: Language-Based Occupational Representations with Large Language Models—0
A novel approach towards the classification of Bone Fracture from Musculoskeletal Radiography images using Attention Based Transfer Learning—0
LAC: Latent Action Composition for Skeleton-based Action Segmentation—0
LAC - Latent Action Composition for Skeleton-based Action Segmentation—0
Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy—0
LAGUNA: LAnguage Guided UNsupervised Adaptation with structured spaces—0
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning—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