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 10251–10275 of 10307 papers

TitleStatusHype
Multi-Domain Sentiment Relevance Classification with Automatic Representation Learning—0
Transfer Learning across Networks for Collective Classification—0
Tensor Representation and Manifold Learning Methods for Remote Sensing Images—0
Adaptive Feature Ranking for Unsupervised Transfer Learning—0
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data—0
Associative embeddings for large-scale knowledge transfer with self-assessment—0
Discriminative Transfer Learning with Tree-based Priors—0
Learning a Deep Compact Image Representation for Visual Tracking—0
Transfer Learning in a Transductive Setting—0
On the definition of a general learning system with user-defined operators—0
Clustering Markov Decision Processes For Continual Transfer—0
A PAC-Bayesian bound for Lifelong Learning—0
Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation—0
Multi-Task Regularization with Covariance Dictionary for Linear Classifiers—0
Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties—0
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks—0
DeCAF: A Deep Convolutional Activation Feature for Generic Visual RecognitionCode0
SuMT: A Framework of Summarization and MT—0
A Study on Bootstrapping Bilingual Vector Spaces from Non-Parallel Data (and Nothing Else)—0
Case Study of Model Adaptation: Transfer Learning and Online Learning—0
POMDP-based dialogue manager adaptation to extended domains—0
Sentiment Relevance—0
Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction—0
Hidden Markov tree models for semantic class induction—0
Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia—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