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Found 10 papers, 3 papers with code
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis
Medical image classification plays a crucial role in computer-aided clinical diagnosis.
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling.
Less is more: Ensemble Learning for Retinal Disease Recognition Under Limited Resources
This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch.
Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework
Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields.
Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey
Furthermore, in relation to the creation and application of foundation models for weather and climate data understanding, we delve into the field's prevailing challenges, offer crucial insights, and propose detailed avenues for future research.
Federated Prompt Learning for Weather Foundation Models on Devices
On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing, holds significance for supporting human activates.
MASK-CNN-Transformer For Real-Time Multi-Label Weather Recognition
The proposed model called MASK-Convolutional Neural Network-Transformer (MASK-CT) is based on the Transformer, the convolutional process, and the MASK mechanism.
TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression
TempEE avoids using auto-regression and instead employs a one-step forward strategy to prevent cumulative error spreading during the extrapolation process.
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data
To relieve the data exposure concern across regions, a novel federated learning approach has been proposed to collaboratively learn a brand-new spatio-temporal Transformer-based foundation model across participants with heterogeneous meteorological data.
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images
The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset.
