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Found 8 papers, 4 papers with code
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI Net), addresses challenges in semantic segmentation, particularly on multiclass imbalanced datasets with limited samples.
Unlocking the capabilities of explainable fewshot learning in remote sensing
While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies.
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts.
Improving Self-supervised Learning for Out-of-distribution Task via Auxiliary Classifier
Observing a strong relationship between rotation prediction (self-supervised) accuracy and semantic classification accuracy on OOD tasks, we introduce an additional auxiliary classification head in our multi-task network along with semantic classification and rotation prediction head.
Scalable Adversarial Online Continual Learning
Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susceptibility to the catastrophic forgetting problem.
Latent Preserving Generative Adversarial Network for Imbalance classification
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem.
Does Adversarial Oversampling Help us?
Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach.
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach.
