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 10201–10225 of 10307 papers

TitleStatusHype
Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition—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
Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean—0
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics—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