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 9026–9050 of 10307 papers

TitleStatusHype
Task Decomposition for Iterative Learning Model Predictive Control—0
Improving Prostate Cancer Detection with Breast Histopathology Images—0
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—0
Dense Classification and Implanting for Few-Shot Learning—0
Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar—0
Practical Semantic Parsing for Spoken Language Understanding—0
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches—0
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
Learning Hierarchical Teaching Policies for Cooperative Agents—0
Machine learning method for single trajectory characterizationCode0
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention—0
Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell ScreeningCode0
ViTOR: Learning to Rank Webpages Based on Visual Features—0
Deep Transfer Learning for Multiple Class Novelty DetectionCode0
Learning from Higher-Layer Feature Visualizations—0
Defining Image Memorability using the Visual Memory Schema—0
PROPS: Probabilistic personalization of black-box sequence modelsCode0
Zero-Shot Task TransferCode0
Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus—0
neuralRank: Searching and ranking ANN-based model repositories—0
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer LearningCode0
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis—0
Optimal Projection Guided Transfer Hashing for Image RetrievalCode0
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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