TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps.   While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations such as spectrograms.   By leveraging this representational advantage, in conjunction with a highly expressive probabilistic model and a multiscale generation procedure, we design a model capable of generating high-fidelity audio samples which capture structure at timescales which time-domain models have yet to achieve.   We demonstrate that our model captures longer-range dependencies than time-domain models such as WaveNet across a diverse set of unconditional generation tasks, including single-speaker speech generation, multi-speaker speech generation, and music generation.

SOLUTION: We introduce an autoregressive generative model for spectrograms and demonstrate applications to speech and music generation

PROBLEM: Optimization algorithms for training deep models not only affects the convergence rate and stability of the training process, but are also highly related to the generalization performance of trained models. While adaptive algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in many scenarios, they often lead to worse generalization performance than SGD, when used for training deep neural networks (DNNs). In this work, we identify two problems regarding the direction and step size for updating the weight vectors of hidden units, which may degrade the generalization performance of Adam. As a solution, we propose the normalized direction-preserving Adam (ND-Adam) algorithm, which controls the update direction and step size more precisely, and thus bridges the generalization gap between Adam and SGD. Following a similar rationale, we further improve the generalization performance in classification tasks by regularizing the softmax logits. By bridging the gap between SGD and Adam, we also shed some light on why certain optimization algorithms generalize better than others.

SOLUTION: A tailored version of Adam for training DNNs, which bridges the generalization gap between Adam and SGD.

PROBLEM: Adversarial perturbations cause a shift in the salient features of an image, which may result in a misclassification. We demonstrate that gradient-based saliency approaches are unable to capture this shift, and develop a new defense which detects adversarial examples based on learnt saliency models instead. We study two approaches: a CNN trained to distinguish between natural and adversarial images using the saliency masks produced by our learnt saliency model, and a CNN trained on the salient pixels themselves as its input. On MNIST, CIFAR-10 and ASSIRA, our defenses are able to detect various adversarial attacks, including strong attacks such as C&W and DeepFool, contrary to gradient-based saliency and detectors which rely on the input image. The latter are unable to detect adversarial images when the L_2- and L_infinity- norms of the perturbations are too small. Lastly, we find that the salient pixel based detector improves on saliency map based detectors as it is more robust to white-box attacks.

SOLUTION:
We show that gradients are unable to capture shifts in saliency due to adversarial perturbations and present an alternative adversarial defense using learnt saliency models that is effective against both black-box and white-box attacks.