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 6976–7000 of 10307 papers

TitleStatusHype
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval—0
An Intrinsically Knowledge-Transferring Developmental Spiking Neural Network for Tactile Classification—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
A Comparison of LSTM and BERT for Small Corpus—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
Text recognition on images using pre-trained CNN—0
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark—0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—0
Text-Speech Language Models with Improved Cross-Modal Transfer by Aligning Abstraction Levels—0
SimDA: Simple Diffusion Adapter for Efficient Video Generation—0
An Interpretable Knowledge Transfer Model for Knowledge Base Completion—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
Similarit\'e s\'emantique entre phrases : apprentissage par transfert interlingue (Semantic Sentence Similarity : Multilingual Transfer Learning)—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
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning—0
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data—0
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning—0
An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction—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