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Conditional Generation of Periodic Signals with Fourier-Based Decoder
Jiyoung Lee · Wonjae Kim · Daehoon Gwak · Edward Choi
Event URL: https://openreview.net/forum?id=y9Sxt1Lpyw »

Periodic signals play an important role in daily lives. Although conventional sequential models have shown remarkable success in various fields, they still come short in modeling periodicity; they either collapse, diverge or ignore details. In this paper, we introduce a novel framework inspired by Fourier series to generate periodic signals. We first decompose the given signals into multiple sines and cosines and then conditionally generate periodic signals with the output components. We have shown our model efficacy on three tasks: reconstruction, imputation and conditional generation. Our model outperforms baselines in all tasks and shows more stable and refined results.

Author Information

Jiyoung Lee (Korea Advanced Institute of Science and Technology)
Wonjae Kim (NAVER AI Lab)
Daehoon Gwak (KAIST)
Edward Choi (KAIST)

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