# Special Issue on Advances in Deep Learning
###### Abstract
However, GAN-based DA requires facing several challenges such as deciding when to stop training, how to properly evaluate the training performance when building a validation set is not possible, and how to guarantee the reliability of the model output in case of significant domain shifts. To soften these issues, the authors of [17] introduce novel confidence metrics to predict the generalization ability of the model and thus stop its training without the need for a separate validation set. Another use of generative models is the development of powerful adversarial attacks, where adding purposefully tiny perturbations to a clean input can lead the classifier to output the label of any desired class. In [18], an efficient method to improve the attack success rate in the most challenging scenario, i.e., when the attacker has low or no knowledge about the model to attack,is presented. The proposed approach exploits data augmentation to train an ensemble of substitute models that are used to craft adversarial examples. Given the relevance of the threat of such adversarial attacks, the survey in [19] tries to resume recent advances in this rapidly growing subject area, from both the defensive and offensive points of view. **One-dimensional signals. ** Besides computer vision applications, deep learning is widespread in one-dimensional signal processing. As an example, in [20], audio speech is enhanced through deep learning. This work presents an interesting denoising approach based on the disentangling of the representation of the useful signal and the noise. These two components are then discriminated in the latent space by a neural network trained through an adversarial paradigm. Another relevant application related to audio processing is recognizing emotions in human speech. This information can then be used in various domains, from customer care to security. The authors of [21] tackle this problem by feeding a Long Short-Term Memory (LSTM) network with spectrograms and handcrafted features capturing voice timber. The final emotion classification is then obtained through a Support Vector Machine. Similar approaches are adopted to address signal classification and information extraction in different domains. As an example, in [22], CNN and particle swarm optimization are jointly employed to predict large-scale wind power, while in [23], a self-attention CNN is used to classify the heartbeat signal of hatching eggs in order to tell apart dead from alive ones in commercial poultry breeding. In [24], the authors present an attention-based deep learning approach to automatic signal modulation recognition, which is robust against transmission errors. Finally, in [25, 26], the classification of vibration signals is carried out through recurrent neural networks. In the first work [25], bearing fault diagnosis is performed to prevent accidents and reduce production costs, while the second paper [26] is concerned with surface roughness prediction for milling process quality assessment. **Text. ** Deep learning approaches also appear in many applications related to text processing, in both structured (e.g., web pages and source code) and unstructured domains (e.g., Natural Language Processing (NLP)). This special issue presents two approaches to structured text processing, both leveraging recurrent models but with very different aims. In [27], track's metadata and user's behavioral data are jointly used to train an LSTM, which learns user preferences and can improve personalized music recommendation. Instead, the authors of [28] try to predict the risk of chronic disease in patients analyzing the data collected from a national survey designed to assess the health and nutritional status of adults and children in Korea. In particular, they built their approach around Char-RNN [29], a deep recurrent network that efficiently models language by also handling missing data in input sequences.