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 10201–10250 of 10307 papers

TitleStatusHype
Metric Imitation by Manifold Transfer for Efficient Vision Applications—0
Lifelong Machine Learning for Topic Modeling and Beyond—0
Zero-Shot Object Recognition by Semantic Manifold Distance—0
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera RelocalizationCode0
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks—0
Recurrent Neural Network Training with Dark Knowledge Transfer—0
Robust Visual Knowledge Transfer via EDA—0
Subset Feature Learning for Fine-Grained Category Classification—0
Abu-MaTran: Automatic building of Machine Translation—0
Compression Artifacts Reduction by a Deep Convolutional NetworkCode0
Correlational Neural NetworksCode0
Transductive Multi-class and Multi-label Zero-shot Learning—0
On the Intrinsic Limits to Representationally-Adaptive Machine-Learning—0
A HMAX with LLC for visual recognition—0
Transductive Multi-view Zero-Shot Learning—0
Bibliometric-enhanced Information Retrieval: 2nd International BIR Workshop—0
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer—0
Learning unbiased features—0
Rediscovering the Alphabet - On the Innate Universal Grammar—0
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning—0
Flexible Transfer Learning under Support and Model Shift—0
Visual Sentiment Prediction with Deep Convolutional Neural Networks—0
Deep Multi-Instance Transfer Learning—0
Probabilistic Models of Cross-Lingual Semantic Similarity in Context Based on Latent Cross-Lingual Concepts Induced from Comparable Data—0
Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean—0
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning—0
Joint Emotion Analysis via Multi-task Gaussian Processes—0
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics—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
Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective—0
Instance-weighted Transfer Learning of Active Appearance Models—0
Inferring Analogous Attributes—0
Linguistic Considerations in Automatic Question Generation—0
COSTA: Co-Occurrence Statistics for Zero-Shot Classification—0
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning—0
Cross-lingual Opinion Analysis via Negative Transfer Detection—0
Model Transport: Towards Scalable Transfer Learning on Manifolds—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
Automatic Wayang Ontology Construction using Relation Extraction from Free Text—0
The impact of near domain transfer on biomedical named entity recognition—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