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 2101–2125 of 10307 papers

TitleStatusHype
Causal Categorization of Mental Health Posts using Transformers—0
A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting—0
CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20—0
Advancing Voice Cloning for Nepali: Leveraging Transfer Learning in a Low-Resource Language—0
A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis—0
CUNI Submission for the Inuktitut Language in WMT News 2020—0
CATrans: Context and Affinity Transformer for Few-Shot Segmentation—0
Adversarial Transfer Learning for Cross-domain Visual Recognition—0
A novel transfer learning method based on common space mapping and weighted domain matching—0
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk—0
A Novel Transfer Learning-Based Approach for Screening Pre-existing Heart Diseases Using Synchronized ECG Signals and Heart Sounds—0
A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field—0
Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge—0
Curricular Transfer Learning for Sentence Encoded Tasks—0
Cataloging Accreted Stars within Gaia DR2 using Deep Learning—0
Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection—0
A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks—0
Case Study of Model Adaptation: Transfer Learning and Online Learning—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation—0
Advancing Roadway Sign Detection with YOLO Models and Transfer Learning—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—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