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The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model All results are either re We term our method mixture generative adversarial nets (mgan).
Explore the openreview platform for hosting and managing academic venues with guides on creating, customizing, and running your venue effectively. Meanwhile, under the same capacity as mgan, our model reduces the fid by 14:61% Egrate edge maps that contain critical textural information to boost synthesis quality
Abstract to address the issues of catastrophic forgetting in continual generative learning (cgl), dominant methods leverage the generative replay strategy
However, they often suffer from high time complexity and inferior generative sample quality In this work, we develop an eficient and effective cgl method via knowledge reconstruction and feedback consolidation (kfc)
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