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]: Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation. Our experiments demonstrate that—without labels—WaveGAN learns to produce intelligible words when trained on a small-vocabulary speech dataset, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano. We compare WaveGAN to a method which applies GANs designed for image generation on image-like audio feature representations, finding both approaches to be promising.
[A]: Learning to synthesize raw waveform audio with GANs


[Q]: Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data. In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods. We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison. We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.
[A]: We pose that generative models' likelihoods are excessively influenced by the input's complexity, and propose a way to compensate it when detecting out-of-distribution inputs


[Q]: Theories in cognitive psychology postulate that humans use similarity as a basis
 for object categorization. However, work in image classification generally as-
 sumes disjoint and equally dissimilar classes to achieve super-human levels of
 performance on certain datasets. In our work, we adapt notions of similarity using
 weak labels over multiple hierarchical levels to boost classification performance.
 Instead of pitting clustering directly against classification, we use a warm-start
 based evaluation to explicitly provide value to a clustering representation by its
 ability to aid classification. We evaluate on CIFAR10 and a fine-grained classifi-
 cation dataset to show improvements in performance with the procedural addition
 of intermediate losses and weak labels based on multiple hierarchy levels. Further-
 more, we show that pretraining AlexNet on hierarchical weak labels in conjunc-
 tion with intermediate losses outperforms a classification baseline by over 17% on
 a subset of Birdsnap dataset. Finally, we show improvement over AlexNet trained
 using ImageNet pre-trained weights as initializations which further supports our 
 claim of the importance of similarity.
[A]:
Cluster before you classify; using weak labels to improve classification