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While developing perception based deep learning models, the benefit of synthetic data is enormous. However, performance of networks trained with synthetic data for certain computer vision tasks degrade significantly when tested on real world data due to the domain gap between them. One of the popular solutions in bridging this gap between synthetic and actual world data is to frame it as a domain adaptation task. In this paper, we propose and evaluate novel ways for the betterment of such approaches. In particular we build upon the method of UNIT-GAN. In normal GAN training for the task of domain translation, pairing of images from both the domains (viz, real and synthetic) is done randomly. We propose a novel method to efficiently incorporate semantic supervision into this pair selection, which helps in boosting the performance of the model along with improving the visual quality of such transformed images. We illustrate our empirical findings on Cityscapes \cite{cityscapes} and challenging synthetic dataset Synscapes. Though the findings are reported on the base network of UNIT-GAN, they can be easily extended to any other similar network.

Skyline queries enable multi-criteria optimization by filtering objects that are worse in all the attributes of interest than another object. To handle the large answer set of skyline queries in high-dimensional datasets, the concept of k-dominance was proposed where an object is said to dominate another object if it is better (or equal) in at least k attributes. This relaxes the full domination criterion of normal skyline queries and, therefore, produces lesser number of skyline objects. This is called the k-dominant skyline set. Many practical applications, however, require that the preferences are applied on a joined relation. Common examples include flights having one or multiple stops, a combination of product price and shipping costs, etc. In this paper, we extend the k-dominant skyline queries to the join paradigm by enabling such queries to be asked on joined relations. We call such queries KSJQ (k-dominant skyline join queries). The number of skyline attributes, k, that an object must dominate is from the combined set of skyline attributes of the joined relation. We show how pre-processing the base relations helps in reducing the time of answering such queries over the naive method of joining the relations first and then running the k-dominant skyline computation. We also extend the query to handle cases where the skyline preference is on aggregated values in the joined relation (such as total cost of the multiple legs of the flight) which are available only after the join is performed. In addition to these problems, we devise efficient algorithms to choose the value of k based on the desired cardinality of the final skyline set. Experiments on both real and synthetic datasets demonstrate the efficiency, scalability and practicality of our algorithms., Comment: Appeared as a short paper in ICDE 2017