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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation
The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data.
Variational Density Propagation Continual Learning
Catastrophic forgetting is mitigated by using the closed-form ELBO to approximate the Minimum Description Length (MDL) Principle, inherently penalizing changes in the model likelihood by minimizing the KL Divergence between the variational posterior for the current task and the previous task's variational posterior acting as the prior.
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
The expansion of explainable artificial intelligence as a field of research has generated numerous methods of visualizing and understanding the black box of a machine learning model.
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations.
