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 5176–5200 of 10307 papers

TitleStatusHype
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets—0
Learning from Higher-Layer Feature Visualizations—0
Learning from LDA using Deep Neural Networks—0
Learning from Synthetic Data for Visual Grounding—0
Multi-View Representation is What You Need for Point-Cloud Pre-Training—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
Learning from scarce information: using synthetic data to classify Roman fine ware pottery—0
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning—0
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation—0
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark—0
Learning Graphs for Knowledge Transfer With Limited Labels—0
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks—0
Learning Hierarchical Teaching Policies for Cooperative Agents—0
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics—0
Learning Image Representations by Completing Damaged Jigsaw Puzzles—0
Learning Implicit Generative Models by Matching Perceptual Features—0
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data—0
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning—0
Learning Invariant Representations across Domains and Tasks—0
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets—0
Learning Losses for Strategic Classification—0
Learning Modality-Invariant Representations for Speech and Images—0
Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer—0
Learning More Generalized Experts by Merging Experts in Mixture-of-Experts—0
Learning Multilingual Topics from Incomparable Corpus—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