After a break of about half a year, i got back to doing 'infinite zoom' experiments using a variety of neural network setups. Here are some snapshots made during my 'travels' in which i iterated through many combinations of network architectures, training methods, hyper-parameter settings and input modes:
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| v17b g5d2 08000_026 |
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| v10b g6d2 079000_011 |
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| v10b g8d2 060000_055 |
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| v12c g6d2 006000_026 |
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| v14b g1d2 065000_026 |
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| v14b g1d2 065000_013 |
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| v16b g1d2 029000_019 |
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| v17b g6d2 019000_052 |
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| v10b g8d2 030000_017 |
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| v12b g5d2 050000_034 |
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| v17b g5d2 013000_039 |
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| v14b g1d2 050000_052 |
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| v10b g8d2 060000_029 |
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| v17b g1d2 025000_053 |
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| v14b g1d2 080000_013 |
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| v17b g5d2 080000_012 |
The image titles are formatted so that they encode important details about the setup that generated the image. Lots of experiments have been done and documenting the output in this way makes it easier to spot the most promising setups when going through the ever growing collection of snapshots.
- v* : train method variant.
- b/c : input sample(s) specifier (b = single, c = multiple).
- g*d* : generator and discriminator variant.
- *_* : trainstep_zoom-depth at the time of the image capture
Stay tuned! This blog is a work under construction.
