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[Access]Every machine learning professional knows that when a model isn’t performing well, the only logical solution is more complexity. In my thesis, I embrace this timeless wisdom by introducing an improved hierarchical quantized autoencoder. This inference-time-configurable, modular deep compression model effortlessly generalizes across multiple domains, including the stupport 1D singal domain. Attention, and a specialized loss function, is all you need.
[Paper] [Code]Images are just fancy 2D data storage, right? In this paper, we exploit that idea by tricking deep image compression models into handling radio frequency. Using a mel spectrogram domain transform, we make RF data look like images and let the compression models do the hard work.
[Paper]How far can you push compression algorithms when you limit your domain? Well, turns out further than you would think as long as keep your promise of domain limitation and are willing to accept some negligable loss of perceptibility. Don't tell Shannon!
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