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CoPE: Conditional image generation using Polynomial Expansions
[article]
2021
arXiv
pre-print
Generative modeling has evolved to a notable field of machine learning. Deep polynomial neural networks (PNNs) have demonstrated impressive results in unsupervised image generation, where the task is to map an input vector (i.e., noise) to a synthesized image. However, the success of PNNs has not been replicated in conditional generation tasks, such as super-resolution. Existing PNNs focus on single-variable polynomial expansions which do not fare well to two-variable inputs, i.e., the noise
arXiv:2104.05077v3
fatcat:3ojtyukuuvahhafpbe2zzdjsry