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 10101–10125 of 10307 papers

TitleStatusHype
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images—0
Identifying individual facial expressions by deconstructing a neural network—0
Experimental and causal view on information integration in autonomous agents—0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance DetectionCode0
WordNet2Vec: Corpora Agnostic Word Vectorization Method—0
Multi-Organ Cancer Classification and Survival Analysis—0
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization—0
Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer—0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification—0
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification—0
SNN: Stacked Neural Networks—0
Domain Transfer Multi-Instance Dictionary Learning—0
Cross Domain Adaptation by Learning Partially Shared Classifiers and Weighting Source Data Points in the Shared Subspaces—0
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification—0
Deep Transfer Learning with Joint Adaptation Networks—0
Neural Dataset GeneralityCode0
Transfer Hashing with Privileged Information—0
Deep Neural Networks Under StressCode0
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts—0
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks—0
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer—0
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics—0
Transfer Learning for Low-Resource Neural Machine TranslationCode0
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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