… a semi-supervised deep learning approach for … the fair comparison problem in regression
problems but not in the classification problems. In order to extend the proposed fair comparison …

… images or time series, while only few deep learning methods are proposed for tabular
information. … We provide an experimental comparison between VFAE and FairSwiRL in Sect. 6. …

… to challenge common wisdom regarding building a deep learning network such as ConvNet
[4], [… In this work, we conducted extensive experiments and fair comparisons of these types of …

… Face recognition via deep learning has achieved a series of breakthrough in these years [25,
27, … For fair comparison, we respectively train three kind of models under the supervision of …

… Therefore, we introduce BenQ to facilitate fair comparisons in two separate race tracks, ie,
intra-comparison of the … compression algorithms, and hardware accelerators for deep learning. …

… In this paper, we propose using deep learning approaches to decode fixed-length and …
with LUT decoding, becomes practical with deep learning-based decoding. We then consider …

… For comparisons, we compute the mutual information when we remove one or both losses.
As demonstrated in Fig. 6, both I(X; C|G) and I(G; C) increase in the first 20 epochs since the …

… Learning fair representation problem has been studied in … In this paper, we propose a fair
representation learning framework … for a fair comparison. Finally we compared our results with …

… for fair scheduling mechanisms in service systems. The Generalized Processor Sharing (GPS)
mechanism has been widely utilized as the fair … Therefore, we propose a deep learning …

… deep learning model with fair adaptive scaling We aimed to devise a fairness learning module
to enhance existing deep learning … Baseline models for comparison We selected seven …