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: While modern  generative  models are able to synthesize high-fidelity, visually appealing images, successfully generating examples that are useful for recognition tasks remains an elusive goal. To this end, our key insight is that the examples should be synthesized to recover classifier decision boundaries that would be learned from a large amount of real examples. More concretely, we treat a classifier trained on synthetic examples as ''student'' and a classifier trained on real examples as ''teacher''. By introducing knowledge distillation into a meta-learning framework, we encourage the generative model to produce examples in a way that enables the student classifier to mimic the behavior of the teacher. To mitigate the potential gap between student and teacher classifiers, we further propose to distill the knowledge in a progressive manner, either by gradually strengthening the teacher or weakening the student. We demonstrate the use of our model-agnostic distillation approach to deal with data scarcity, significantly improving few-shot learning performance on miniImageNet and ImageNet1K benchmarks.

A: This paper introduces progressive knowledge distillation for learning generative models that are recognition task oriented
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Q: Routing models, a form of conditional computation where examples are routed through a subset of components in a larger network, have shown promising results in recent works. Surprisingly, routing models to date have lacked important properties, such as architectural diversity and large numbers of routing decisions. Both architectural diversity and routing depth can increase the representational power of a routing network. In this work, we address both of these deficiencies. We discuss the significance of architectural diversity in routing models, and explain the tradeoffs between capacity and optimization when increasing routing depth. In our experiments, we find that adding architectural diversity to routing models significantly improves performance, cutting the error rates of a strong baseline by 35% on an Omniglot setup. However, when scaling up routing depth, we find that modern routing techniques struggle with optimization. We conclude by discussing both the positive and negative results, and suggest directions for future research.

A: Per-example routing models benefit from architectural diversity, but still struggle to scale to a large number of routing decisions.
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Q: The goal of generative models is to model the underlying data distribution of a
 sample based dataset. Our intuition is that an accurate model should in principle
 also include the sample based dataset as part of its induced probability distribution.
 To investigate this, we look at fully trained generative models using the Generative
 Adversarial Networks (GAN) framework and analyze the resulting generator
 on its ability to memorize the dataset. Further, we show that the size of the initial
 latent space is paramount to allow for an accurate reconstruction of the training
 data. This gives us a link to compression theory, where Autoencoders (AE) are
 used to lower bound the reconstruction capabilities of our generative model. Here,
 we observe similar results to the perception-distortion tradeoff (Blau & Michaeli
 (2018)). Given a small latent space, the AE produces low quality and the GAN
 produces high quality outputs from a perceptual viewpoint. In contrast, the distortion
 error is smaller for the AE. By increasing the dimensionality of the latent
 space the distortion decreases for both models, but the perceptual quality only
 increases for the AE.

A:
We analyze the impact of the latent space of fully trained generators by pseudo inverting them.
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