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 9426–9450 of 10307 papers

TitleStatusHype
Farewell Freebase: Migrating the SimpleQuestions Dataset to DBpediaCode0
Transfer Learning for Entity Recognition of Novel ClassesCode0
Transferred Embeddings for Igbo Similarity, Analogy, and Diacritic Restoration Tasks—0
Using Neural Transfer Learning for Morpho-syntactic Tagging of South-Slavic Languages Tweets—0
Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources—0
TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING'18—0
Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer LearningCode0
Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet)Code0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Improving Transferability of Deep Neural Networks—0
Semi-supervised Transfer Learning for Image Rain RemovalCode0
Visual Analogies between Atari Games for Studying Transfer Learning in RL—0
Attend Before you Act: Leveraging human visual attention for continual learningCode0
Do Better ImageNet Models Transfer Better... for Image Recommendation?—0
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks—0
Asynchronous Advantage Actor-Critic Agent for Starcraft II—0
Knowledge-based Transfer Learning ExplanationCode0
Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features—0
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding—0
Deep Transfer Learning for Cross-domain Activity Recognition—0
Bio-Measurements Estimation and Support in Knee Recovery through Machine Learning—0
Visual Domain Adaptation with Manifold Embedded Distribution Alignment—0
Transfer Learning for Action Unit Recognition—0
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement—0
Metric Embedding Autoencoders for Unsupervised Cross-Dataset Transfer Learning—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