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 10226–10250 of 10307 papers

TitleStatusHype
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning—0
Probabilistic Models of Cross-Lingual Semantic Similarity in Context Based on Latent Cross-Lingual Concepts Induced from Comparable Data—0
Joint Emotion Analysis via Multi-task Gaussian Processes—0
Transfer Prototype-based Fuzzy Clustering—0
Transfer Learning for Video Recognition with Scarce Training Data for Deep Convolutional Neural Network—0
Scalable Greedy Algorithms for Transfer Learning—0
Dependency Parsing: Past, Present, and Future—0
Collaborative Recommendation with Auxiliary Data: A Transfer Learning View—0
Web-Scale Training for Face Identification—0
Semantic Graph for Zero-Shot Learning—0
Instance-weighted Transfer Learning of Active Appearance Models—0
Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective—0
Linguistic Considerations in Automatic Question Generation—0
COSTA: Co-Occurrence Statistics for Zero-Shot Classification—0
Cross-lingual Opinion Analysis via Negative Transfer Detection—0
Model Transport: Towards Scalable Transfer Learning on Manifolds—0
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning—0
Inferring Analogous Attributes—0
Development of a TV Broadcasts Speech Recognition System for Qatari Arabic—0
New Directions for Language Resource Development and Distribution—0
Using Transfer Learning to Assist Exploratory Corpus Annotation—0
Transfer learning of feedback head expressions in Danish and Polish comparable multimodal corpora—0
MEG Decoding Across Subjects—0
The impact of near domain transfer on biomedical named entity recognition—0
Automatic Wayang Ontology Construction using Relation Extraction from Free Text—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