Etmann et al. Connection between robustness and interpretability On the Connection Between Adversarial Robustness and Saliency Map Interpretability Advantage and Disadvantages of adversarial training? While this method – like all known approaches of defense – decreases the accuracy of the classifier, it is also successful in increasing the robustness to adversarial attacks Connections between the interpretability of saliency maps and robustness? saliency maps of robustified classifiers tend to be far more interpretable, in that structures in the input image also emerge in the corresponding saliency map How to obtain saliency maps for a non-robustified networks? In order to obtain a semantically meaningful visualization of the network’s classification decision in non-robustified networks, the saliency map has to be aggregated over many different points in the vicinity of the input image. This can be achieved either via averaging saliency maps of noisy versions of the image (Smilkov
Probabilistic Model-Agnostic Meta-Learning Reading Notes: Probabilistic Model-Agnostic Meta-Learning This post is a reading note for the paper "Probabilistic Model-Agnostic Meta-Learning" by Finn et al. It is a successive work to the famous MAML paper , and can be viewed as the Bayesian version of the MAML model. Introduction When dealing with different tasks of the same family, for example, the image classification family, the neural language processing family, etc.. It is usually preferred to be able to acquire solutions to complex tasks from only a few samples given the past knowledge of other tasks as a prior (few shot learning). The idea of learning-to-learn, i.e., meta-learning, is such a framework. What is meta-learning? The model-agnostic meta-learning (MAML) [1] is a few shot meta-learning algorithm that uses gradient descent to adapt the model at meta-test time to a new few-shot task, and trains the model parameters at meta-training time to enable rapid adap