|
|
|
|
|
|
by aaronsnoswell
3476 days ago
|
|
|
The exiting thing for me is at 1:30 in the video. Smooth changes in the latent space lead to smooth changes in the shape (eg. the chair arms gradually recede along their longitudinal axis). This means you have good generalisability (the network is robust to input noise) and composablility (the network can produce novel results through transformations in latent space). |
|
|