Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions.

Generativeadversarial networks (GANs) were originally envisioned asunsupervised generative models that learn to follow a target distribution.

We thank the reviewers for their valuable feedback. We will add a few sentences in the Introduction of the final paper to further emphasize this point. A comparative assessment will be included in the final paper. R1.4 Typo errors in the caption of Figure 1: We have rectified the typographical error. Unlike discriminative learning approaches, the emphasis here is on the generative aspect (cf. Also refer to R1.1 and R1.5 above.

Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions.

One of the fastest-growing domains in AI is healthcare. Given its importance, it has been the interest of many researchers to deploy ML models into the ever-demanding healthcare domain to aid doctors and increase accessibility. Delivering reliable models, however, demands a sizable amount of data, and the recent COVID-19 pandemic served as a reminder of the rampant and scary nature of healthcare that makes training models difficult. To alleviate such scarcity, many published works attempted to synthesize radiological cough data to train better COVID-19 detection models on the respective radiological data. To accommodate the time sensitivity expected during a pandemic, this work focuses on detecting COVID-19 through coughs using synthetic data to improve the accuracy of the classifier. The work begins by training a CNN on a balanced subset of the Coughvid dataset, establishing a baseline classification test accuracy of 72%. The paper demonstrates how an Auxiliary Classification GAN (ACGAN) may be trained to conditionally generate novel synthetic Mel Spectrograms of both healthy and COVID-19 coughs. These coughs are used to augment the training dataset of the CNN classifier, allowing it to reach a new test accuracy of 75%. The work highlights the expected messiness and inconsistency in training and offers insights into detecting and handling such shortcomings.

Recent breakthroughs in large-scale generative modeling have demonstrated the potential of foundation models in domains such as natural language, computer vision, and protein structure prediction. However, their application in the energy and smart grid sector remains limited due to the scarcity and heterogeneity of high-quality data. In this work, we propose a method for creating high-fidelity electricity consumption time series data for rare and unseen context variables (e.g. location, building type, photovoltaics). Our approach, Context Encoding and Normalizing Time Series Generation, or CENTS, includes three key innovations: (i) A context normalization approach that enables inverse transformation for time series context variables unseen during training, (ii) a novel context encoder to condition any state-of-the-art time-series generator on arbitrary numbers and combinations of context variables, (iii) a framework for training this context encoder jointly with a time-series generator using an auxiliary context classification loss designed to increase expressivity of context embeddings and improve model performance. We further provide a comprehensive overview of different evaluation metrics for generative time series models. Our results highlight the efficacy of the proposed method in generating realistic household-level electricity consumption data, paving the way for training larger foundation models in the energy domain on synthetic as well as real-world data.

I have read the authors' rebuttal. I am satisfied with the answers. I will keep my rating at 8. ---------- Questions / criticisms / suggestions: - I see that in your work you present ACGAN as being a particular instantiation of a cGAN (i.e. For instance, in cGAN the discriminator d(x,y) is estimating p(x y)p(y) (which the generator tries to match with its conditional q(x y)), whereas in ACGAN it is implicitly estimating p(y x)p(x), where p(y x) is the auxiliary classifier and p(x) is the discriminator. It would be important to make this clear since these techniques are sufficiently different from each other.