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

Showing 15011525 of 10307 papers

TitleStatusHype
High Quality Monocular Depth Estimation via Transfer LearningCode1
Few-Shot Learning via Embedding Adaptation with Set-to-Set FunctionsCode1
Meta-Transfer Learning for Few-Shot LearningCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
Transferring Knowledge across Learning ProcessesCode1
Transfer learning for time series classificationCode1
Transfer Learning in Multilingual Neural Machine Translation with Dynamic VocabularyCode1
Contour Knowledge Transfer for Salient Object DetectionCode1
Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment ApproachCode1
How emotional are you? Neural Architectures for Emotion Intensity Prediction in MicroblogsCode1
Deep Recurrent Neural Networks for ECG Signal DenoisingCode1
The Natural Language Decathlon: Multitask Learning as Question AnsweringCode1
Large Scale Fine-Grained Categorization and Domain-Specific Transfer LearningCode1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree SearchCode1
On the effectiveness of task granularity for transfer learningCode1
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language UnderstandingCode1
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural NetworksCode1
Universal Sentence EncoderCode1
Efficient parametrization of multi-domain deep neural networksCode1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video ClassificationCode1
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label NoiseCode1
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy SearchCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Like What You Like: Knowledge Distill via Neuron Selectivity TransferCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified