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.

[EX Q]: Inspired by neurophysiological discoveries of navigation cells in the mammalian
brain, we introduce the first deep neural network architecture for modeling Egocentric
Spatial Memory (ESM). It learns to estimate the pose of the agent and
progressively construct top-down 2D global maps from egocentric views in a spatially
extended environment. During the exploration, our proposed ESM network
model updates belief of the global map based on local observations using a recurrent
neural network. It also augments the local mapping with a novel external
memory to encode and store latent representations of the visited places based on
their corresponding locations in the egocentric coordinate. This enables the agents
to perform loop closure and mapping correction. This work contributes in the
following aspects: first, our proposed ESM network provides an accurate mapping
ability which is vitally important for embodied agents to navigate to goal locations.
In the experiments, we demonstrate the functionalities of the ESM network in
random walks in complicated 3D mazes by comparing with several competitive
baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM)
algorithms. Secondly, we faithfully hypothesize the functionality and the working
mechanism of navigation cells in the brain. Comprehensive analysis of our model
suggests the essential role of individual modules in our proposed architecture and
demonstrates efficiency of communications among these modules. We hope this
work would advance research in the collaboration and communications over both
fields of computer science and computational neuroscience.
[EX A]: first deep neural network for modeling Egocentric Spatial Memory inspired by neurophysiological discoveries of navigation cells in mammalian brain

[EX Q]: Voice Conversion (VC) is a task of converting perceived speaker identity from a source speaker to a particular target speaker. Earlier approaches in the literature primarily find a mapping between the given source-target speaker-pairs. Developing mapping techniques for many-to-many VC using non-parallel data, including zero-shot learning remains less explored areas in VC. Most of the many-to-many VC architectures require training data from all the target speakers for whom we want to convert the voices. In this paper, we propose a novel style transfer architecture, which can also be extended to generate voices even for target speakers whose data were not used in the training (i.e., case of zero-shot learning). In particular, propose Adaptive Generative Adversarial Network (AdaGAN), new architectural training procedure help in learning normalized speaker-independent latent representation, which will be used to generate speech with different speaking styles in the context of VC. We compare our results with the state-of-the-art StarGAN-VC architecture. In particular, the AdaGAN achieves 31.73%, and 10.37% relative improvement compared to the StarGAN in MOS tests for speech quality and speaker similarity, respectively. The key strength of the proposed architectures is that it yields these results with less computational complexity. AdaGAN is 88.6% less complex than StarGAN-VC in terms of FLoating Operation Per Second (FLOPS), and 85.46% less complex in terms of trainable parameters.
[EX A]: Novel adaptive instance normalization based GAN framework for non parallel many-to-many and zero-shot VC.

[EX Q]: Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work. But the different measures provide divergent estimates of selectivity, and this has led to different conclusions regarding the conditions in which selective object representations are learned and the functional relevance of these representations. In an attempt to better characterize object selectivity, we undertake a comparison of various selectivity measures on a large set of units in AlexNet, including localist selectivity, precision, class-conditional mean activity selectivity (CCMAS), network dissection, the human interpretation of activation maximization (AM) images, and standard signal-detection measures. We find that the different measures provide different estimates of object selectivity, with precision and CCMAS measures providing misleadingly high estimates. Indeed, the most selective units had a poor hit-rate or a high false-alarm rate (or both) in object classification, making them poor object detectors. We fail to find any units that are even remotely as selective as the 'grandmother cell' units reported in recurrent neural networks. In order to generalize these results, we compared selectivity measures on a few units in VGG-16 and GoogLeNet trained on the ImageNet or Places-365 datasets that have been described as 'object detectors'. Again, we find poor hit-rates and high false-alarm rates for object classification.
[EX A]:
Looking for object detectors using many different selectivity measures; CNNs are slightly selective , but not enough to be termed object detectors.