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Interesting. It sounds like the idea is fundamentally like factoring out knowledge of "real image" structure into a neutral net. In a way, this is similar to th
by acjohnson55 7y ago
Interesting. It sounds like the idea is fundamentally like factoring out knowledge of "real image" structure into a neutral net. In a way, this is similar to the perceptual models used to discard data in lossy compression.
- dehrmann 7y agoI wonder if there's a way to do this more like traditional compression; performance is a huge issue for compression, and taking inspiration from a neural network might be better than actually using one. Conceptually, this is like a learned dictionary that's captured by the neural net, it's just that this is fuzzier.
- retrac 7y agoTraining the model is extremely expensive computationally, but using it often isn't. For example, StyleGAN takes months of compute-time on a cluster of high-end GPUs to train to get the photorealistic face model we've all seen. But generating new faces from the trained model only takes mere seconds on a low-end GPU or even a CPU.