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 10076–10100 of 10307 papers

TitleStatusHype
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer—0
How should we evaluate supervised hashing?Code0
Transfer Learning for Material Classification using Convolutional Networks—0
Towards Deep Symbolic Reinforcement Learning—0
An Iterative Transfer Learning Based Ensemble Technique for Automatic Short Answer Grading—0
Transfer String Kernel for Cross-Context DNA-Protein Binding PredictionCode0
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes—0
What makes ImageNet good for transfer learning?Code0
Transfer Learning for Endoscopic Image Classification—0
A novel transfer learning method based on common space mapping and weighted domain matching—0
Generative Knowledge Transfer for Neural Language Models—0
Deep Convolution Networks for Compression Artifacts ReductionCode0
Cross-Lingual Image Caption Generation—0
A Two-stage Approach for Extending Event Detection to New Types via Neural Networks—0
Cross-domain Text Classification with Multiple Domains and Disparate Label Sets—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
Modeling Social Norms Evolution for Personalized Sentiment Classification—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
Language Transfer Learning for Supervised Lexical Substitution—0
Letter Sequence Labeling for Compound Splitting—0
Transductive Adaptation of Black Box Predictions—0
Towards Generalizable Sentence Embeddings—0
Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training—0
Re-presenting a Story by Emotional Factors using Sentimental Analysis Method—0
Effects of Additional Data on Bayesian Clustering—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