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 3101–3150 of 10307 papers

TitleStatusHype
Differentially Private Video Activity Recognition—0
Transferability Metrics for Object DetectionCode0
Semi-supervised Multimodal Representation Learning through a Global WorkspaceCode0
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data—0
A Collaborative Transfer Learning Framework for Cross-domain Recommendation—0
Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition—0
Deep Transfer Learning for Intelligent Vehicle Perception: a Survey—0
Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary—0
Parameter-Level Soft-Masking for Continual LearningCode1
Feature Adversarial Distillation for Point Cloud Classification—0
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial DiversityCode0
Semi-supervised Object Detection: A Survey on Recent Research and Progress—0
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented TasksCode4
Cross-domain Recommender Systems via Multimodal Domain Adaptation—0
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning—0
Variance-Covariance Regularization Improves Representation Learning—0
TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible AdapterCode0
Transferable Curricula through Difficulty Conditioned Generators—0
Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review—0
A systematic approach to deep learning-based nodule detection in chest radiographsCode1
Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection—0
Wildfire Detection Via Transfer Learning: A Survey—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Introspective Action Advising for Interpretable Transfer Learning—0
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks—0
Knowledge Distillation via Token-level Relationship Graph—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning—0
Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions—0
MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language ModelsCode0
Masking meets Supervision: A Strong Learning AllianceCode1
MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in IndonesianCode0
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous DatasetsCode1
Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives—0
Knowledge Transfer-Driven Few-Shot Class-Incremental LearningCode0
Transformer Training Strategies for Forecasting Multiple Load Time SeriesCode0
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotationsCode1
Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imagingCode0
Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models—0
Text-Driven Foley Sound Generation With Latent Diffusion ModelCode0
Neural Priming for Sample-Efficient AdaptationCode1
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions—0
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient LearningCode1
Cross-corpus Readability Compatibility Assessment for English TextsCode0
Segment Any Point Cloud Sequences by Distilling Vision Foundation ModelsCode2
Modeling T1 Resting-State MRI Variants Using Convolutional Neural Networks in Diagnosis of OCDCode0
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks—0
DocumentNet: Bridging the Data Gap in Document Pre-Training—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset—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