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 6676–6700 of 10307 papers

TitleStatusHype
Critically examining the Domain Generalizability of Facial Expression Recognition models—0
Domain Adaptation Broad Learning System Based on Locally Linear Embedding—0
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
A Knowledge-Grounded Dialog System Based on Pre-Trained Language Models—0
PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework—0
Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction—0
Saying the Unseen: Video Descriptions via Dialog AgentsCode0
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning—0
The Option Keyboard: Combining Skills in Reinforcement Learning—0
Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks—0
Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource languageCode0
Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems—0
Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning—0
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual LearningCode0
Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning—0
Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data TransformationsCode0
Near-Optimal Linear Regression under Distribution Shift—0
On the importance of cross-task features for class-incremental learningCode0
Prompt Tuning or Fine-Tuning - Investigating Relational Knowledge in Pre-Trained Language ModelsCode0
Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of SeizuresCode0
Towards Better Shale Gas Production Forecasting Using Transfer Learning—0
CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head RedirectionCode0
Data Optimisation for a Deep Learning Recommender System—0
Do sound event representations generalize to other audio tasks? A case study in audio transfer learning—0
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery—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