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 7601–7650 of 10307 papers

TitleStatusHype
A General Class of Transfer Learning Regression without Implementation Cost—0
ELSIM: End-to-end learning of reusable skills through intrinsic motivation—0
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Supervised Understanding of Word Embeddings—0
Limits of Transfer Learning—0
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery—0
Meta Transfer Learning for Emotion Recognition—0
Advantages of biologically-inspired adaptive neural activation in RNNs during learning—0
Convolutional-network models to predict wall-bounded turbulence from wall quantities—0
Learning compact generalizable neural representations supporting perceptual grouping—0
Generalisation Guarantees for Continual Learning with Orthogonal Gradient DescentCode1
Generalized Zero and Few-Shot Transfer for Facial Forgery Detection—0
On the Theory of Transfer Learning: The Importance of Task Diversity—0
Adversarial Transfer of Pose Estimation Regression—0
Unsupervised Image Classification for Deep Representation LearningCode0
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms—0
Self-Supervised Prototypical Transfer Learning for Few-Shot ClassificationCode1
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?Code0
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud—0
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles—0
Neural Topic Modeling with Continual Lifelong LearningCode1
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19Code1
Unified Representation Learning for Efficient Medical Image Analysis—0
Deep Learning Enabled Semantic Communication SystemsCode1
Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax OptimalityCode1
Deep Categorization with Semi-Supervised Self-Organizing MapsCode0
Learning a functional control for high-frequency finance—0
Delta Schema Network in Model-based Reinforcement LearningCode0
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural NetworksCode0
Response by the Montreal AI Ethics Institute to the European Commission's Whitepaper on AI—0
Improving accuracy and speeding up Document Image Classification through parallel systemsCode1
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated TasksCode0
Using Mobility for Electrical Load Forecasting During the COVID-19 PandemicCode0
Domain Adaptation with Joint Learning for Generic, Optical Car Part Recognition and Detection Systems (Go-CaRD)—0
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks—0
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation—0
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesCode1
Distant Transfer Learning via Deep Random Walk—0
Adversarial Self-Supervised Contrastive LearningCode1
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer—0
Importance Weighting with a Adversarial Network for Large-Scale Sleep Staging—0
MemeSem:A Multi-modal Framework for Sentimental Analysis of Meme via Transfer LearningCode1
Similarity-based transfer learning of decision policies—0
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation—0
Learning the Travelling Salesperson Problem Requires Rethinking GeneralizationCode1
Knowledge Distillation Meets Self-SupervisionCode1
Mutual Information Based Knowledge Transfer Under State-Action Dimension MismatchCode0
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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