Aliasing is a fundamental distortion inherent in all discrete signal processing systems, arising from the violation of the Nyquist-Shannon sampling theorem. While traditionally mitigated in audio and image processing through anti-aliasing filters, the advent of deep learning—specifically in generative modeling—has highlighted critical "texture sticking" artifacts caused by implicit aliasing in neural network layers. This paper provides a detailed examination of the sources of aliasing in continuous and discrete domains, classical solutions, and the modern paradigm shift towards alias-free architectures in machine learning.
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Standard transposed convolutions (upsampling) create "checkerboard artifacts" due to uneven overlap. Strided convolutions (downsampling) cause aliasing. Aliasing is a fundamental distortion inherent in all