Sequence Generation via Subsequence Similarity: Theory and Application to UAV Identification. (arXiv:2301.08403v1 [cs.LG])

The ability to generate synthetic sequences is crucial for a wide range of
applications, and recent advances in deep learning architectures and generative
frameworks have greatly facilitated this process. Particularly, unconditional
one-shot generative models constitute an attractive line of research that
focuses on capturing the internal information of a single image, video, etc. to
generate samples with similar contents. Since many of those one-shot models are
shifting toward efficient non-deep and non-adversarial approaches, we examine
the versatility of a one-shot …

application applications deep learning etc frameworks generative identification information internal process research similarity single synthetic theory video