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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9476–9500 of 10307 papers

TitleStatusHype
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP—0
A Situated Dialogue System for Learning Structural Concepts in Blocks World—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline—0
Transfer Learning via Learning to Transfer—0
Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly ApplicableCode0
TextWorld: A Learning Environment for Text-based GamesCode0
Neural Networks Trained to Solve Differential Equations Learn General Representations—0
A probabilistic constrained clustering for transfer learning and image category discovery—0
A Multimodal Recommender System for Large-scale Assortment Generation in E-commerce—0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection—0
Estimating Bicycle Route Attractivity from Image Data—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Considerations for a PAP Smear Image Analysis System with CNN Features—0
Zeta Distribution and Transfer Learning Problem—0
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts—0
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural NetworksCode0
Uncertainty in Multitask Transfer Learning—0
Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer—0
Towards an efficient deep learning model for musical onset detectionCode0
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation—0
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and GeometryCode0
Action Learning for 3D Point Cloud Based Organ Segmentation—0
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable RepresentationsCode0
Motion Planning NetworksCode0
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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