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 Input:
We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know. These techniques open a wide range of potential applications such as cross-language communication, language learning or automatic video dubbing. We call this general problem multi-language speaker-conditioned speech synthesis and we present a simple but strong baseline for it.

 Our model architecture is similar to the encoder-decoder Char2Wav model or Tacotron. The main difference is that, instead of conditioning on characters or phonemes that are specific to a given language, we condition on a shared phonetic representation that is universal to all languages. This cross-language phonetic representation of text allows to synthesize speech in any language while preserving the vocal characteristics of the original speaker. Furthermore, we show that fine-tuning the weights of our model allows us to extend our results to speakers outside of the training dataset.

Ex Output:
We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know.


Ex Input:
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.

Ex Output:
This paper introduces progressive knowledge distillation for learning generative models that are recognition task oriented


Ex Input:
Many real-world time series, such as in activity recognition, finance, or climate science, have changepoints where the system's structure or parameters change. Detecting changes is important as they may indicate critical events. However, existing methods for changepoint detection face challenges when (1) the patterns of change cannot be modeled using simple and predefined metrics, and (2) changes can occur gradually, at multiple time-scales. To address this, we show how changepoint detection can be treated as a supervised learning problem, and propose a new deep neural network architecture that can efficiently identify both abrupt and gradual changes at multiple scales. Our proposed method, pyramid recurrent neural network (PRNN), is designed to be scale-invariant, by incorporating wavelets and pyramid analysis techniques from multi-scale signal processing. Through experiments on synthetic and real-world datasets, we show that PRNN can detect abrupt and gradual changes with higher accuracy than the state of the art and can extrapolate to detect changepoints at novel timescales that have not been seen in training.

Ex Output:

We introduce a scale-invariant neural network architecture for changepoint detection in multivariate time series.


