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.

Q: We propose a rejection sampling scheme using the discriminator of a GAN to
approximately correct errors in the GAN generator distribution. We show that
under quite strict assumptions, this will allow us to recover the data distribution
exactly. We then examine where those strict assumptions break down and design a
practical algorithmcalled Discriminator Rejection Sampling (DRS)that can be
used on real data-sets. Finally, we demonstrate the efficacy of DRS on a mixture of
Gaussians and on the state of the art SAGAN model. On ImageNet, we train an
improved baseline that increases the best published Inception Score from 52.52 to
62.36 and reduces the Frechet Inception Distance from 18.65 to 14.79. We then use
DRS to further improve on this baseline, improving the Inception Score to 76.08
and the FID to 13.75.

A: We use a GAN discriminator to perform an approximate rejection sampling scheme on the output of the GAN generator.
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Q: Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures. Based on the observation that the magnitude-based pruning indeed minimizes the Frobenius distortion of a linear operator corresponding to a single layer, we develop a simple pruning method, coined lookahead pruning, by extending the single layer optimization to a multi-layer optimization. Our experimental results demonstrate that the proposed method consistently outperforms the magnitude pruning on various networks including VGG and ResNet, particularly in the high-sparsity regime.

A: We study a multi-layer generalization of the magnitude-based pruning.
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Q: Deep generative models such as Generative Adversarial Networks (GANs) and
Variational Auto-Encoders (VAEs) are important tools to capture and investigate
the properties of complex empirical data. However, the complexity of their inner
elements makes their functionment challenging to assess and modify. In this
respect, these architectures behave as black box models. In order to better
understand the function of such networks, we analyze their modularity based on
the counterfactual manipulation of their internal variables. Our experiments on the
generation of human faces with VAEs and GANs support that modularity between
activation maps distributed over channels of generator architectures is achieved
to some degree, can be used to better understand how these systems operate and allow meaningful transformations of the generated images without further training.
erate and edit the content of generated images.

A:
We investigate the modularity of deep generative models.
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