> For the complete documentation index, see [llms.txt](https://elephantmipt.gitbook.io/cyclegan/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://elephantmipt.gitbook.io/cyclegan/future-works.md).

# Future works

### Channel pruning

First of all I think student network could be more compressed. I would somehow choose the architecture depending on teacher model. For example I could use structured pruning to reduce number of channels in residual blocks. I would remain downsampling and upsampling layers the same as it play an encoder-decoder role to/from feature space. The loss between hidden states, therefore, would counts through one conv layer with kernel size 1 to match number of channels.

### Teacher improvements

If I had enough time I would train my network for 200 epochs instead of 100 as it was proposed in original CycleGAN paper. But it would take about 5 days to do so.