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Found 8 papers, 1 papers with code
Exploring Robust Architectures for Deep Artificial Neural Networks
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.
Constrained State Estimation -- A Review
The real-world applications in signal processing generally involve estimating the system state or parameters in nonlinear, non-Gaussian dynamic systems.
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation
Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance.
Robust Learning via Ensemble Density Propagation in Deep Neural Networks
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs).
Self-Compression in Bayesian Neural Networks
We show that Bayesian neural networks automatically discover redundancy in model parameters, thus enabling self-compression, which is linked to the propagation of uncertainty through the layers of the network.
SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks
Moreover, the uncertainty map of the proposed SUPER-Net associates low confidence (or equivalently high uncertainty) to patches in the test input images that are corrupted with noise, artifacts, or adversarial attacks.
Efficient Scopeformer: Towards Scalable and Rich Feature Extraction for Intracranial Hemorrhage Detection
Hybrid CNNs and ViTs might provide the desired feature richness for developing accurate medical computer vision models
The Importance of Robust Features in Mitigating Catastrophic Forgetting
In this paper, we introduce the CL robust dataset and train four baseline models on both the standard and CL robust datasets.
