This work was done while M. Menart was at The Ohio State University.

The code is availableat tt s t s q.

Next, we would like to prove the opposite direction. All LeakyReLU activations have a slope of 0.02 for negative values except when we use a classic discriminator for single image training, for which we use a slope of 0.2. Additionally, the generator's last conv-block activation at each scale is Tanh instead of ReLU and the discriminator's last We clip the gradient s.t it has a maximal L2 norm of 1 for both the generators and Batch sizes of 16 were used for all experiments involving a dataset of images. At test time, the GPU memory usage is significantly reduced and requires 5GB. In this section, we consider training our method with a "frozen" pretrained ResNet34 i.e., optimizing If the problem could be learned with a "small enough" depth, our method would benefit from even As can be seen, our method yields realistic results with any batch size.

However, its application to Q-learning has been limited due to the presence of the max-operator, which makes the associated ODE model a complex nonlinear system. In contrast, the associated ODE of TD learning for policy evaluation is a linear system, whose asymptotic stability is much easier to analyze in general.

In continual learning (CL), we incrementally adapt a model to learn tasks (defined according to the problem at hand) observed sequentially. CL has two main objectives: maintain long-term memory (remember previous tasks) and navigate new experiences continually (quickly adapt to newtasks).

Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where malicious human-imperceptible perturbations are included inthe input to the deep network to fool it into making a wrong classification.