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 9551–9575 of 10307 papers

TitleStatusHype
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection—0
Morphological analysis using a sequence decoderCode0
Accelerated Bayesian Optimization throughWeight-Prior Tuning—0
Object Localization with a Weakly Supervised CapsNet—0
SNU_IDS at SemEval-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning ComprehensionCode0
Learning Time-Sensitive Strategies in Space FortressCode0
Cross-domain attribute representation based on convolutional neural network—0
Optimization of Transfer Learning for Sign Language Recognition Targeting Mobile Platform—0
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature VectorsCode0
General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learningCode0
Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in imagesCode0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—0
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge TransferCode0
Semi-Supervised Domain Adaptation with Representation Learning for Semantic Segmentation across Time—0
Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations—0
Image Based Fashion Product Recommendation with Deep Learning—0
DocFace: Matching ID Document Photos to SelfiesCode0
Transfer Learning of Artist Group Factors to Musical Genre ClassificationCode0
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents—0
Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification—0
Exploring the Limits of Weakly Supervised PretrainingCode0
Boosting Self-Supervised Learning via Knowledge Transfer—0
English-Basque Statistical and Neural Machine Translation—0
Improving Machine Translation of Educational Content via Crowdsourcing—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