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Continuous path keyboard input has higher inherent ambiguity than standard tapping, because the path trace may exhibit not only local overshoots/undershoots (as in tapping) but also, depending on the user, substantial mid-path excursions. Deploying a robust solution thus requires a large amount of high-quality training data, which is difficult to collect/annotate. In this work, we address this challenge by using GANs to augment our training corpus with user-realistic synthetic data. Experiments show that, even though GAN-generated data does not capture all the characteristics of real user data, it still provides a substantial boost in accuracy at a 5:1 GAN-to-real ratio. GANs therefore inject more robustness in the model through greatly increased word coverage and path diversity.

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Apple at ICASSP 2020

Apple sponsored the 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) in May 2020. With a focus on signal processing and its applications, the conference took place virtually from May 4 - 8. Read Apple’s accepted papers below.

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Learning from Simulated and Unsupervised Images through Adversarial Training

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output…
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