EPISODE · Mar 4, 2017 · 5 MIN
Image Generation - Google DeepMind paper with TensorFlow - Deep Learning: Zero to One
from Deep Learning: Zero to One · host Sam Putnam
I talk through generating an image of IRS tax return characters using a model trained on the IRS tax return dataset - NMIST. The authors trained for 70 hours on 32 GPUs. I used unconditioned image generation to create an image in 6 hours on my MacBook Pro CPU. I used the TensorFlow implementation of Conditional Image Generation with PixelCNN Decoders (https://arxiv.org/abs/1606.05328) by a student named Anant Gupta and learned that reasonable-looking digits can be generated with significantly fewer training steps, as soon as the training loss approaches that reached by the DeepMind authors. Each step is detailed at https://medium.com/@SamPutnam/this-is-the-1st-deep-learning-zero-to-one-newsletter-this-one-is-called-image-generation-935bcaf0f37c
What this episode covers
I talk through generating an image of IRS tax return characters using a model trained on the IRS tax return dataset - NMIST. The authors trained for 70 hours on 32 GPUs. I used unconditioned image generation to create an image in 6 hours on my MacBook Pro CPU. I used the TensorFlow implementation of Conditional Image Generation with PixelCNN Decoders (https://arxiv.org/abs/1606.05328) by a student named Anant Gupta and learned that reasonable-looking digits can be generated with significantly fewer training steps, as soon as the training loss approaches that reached by the DeepMind authors. Each step is detailed at https://medium.com/@SamPutnam/this-is-the-1st-deep-learning-zero-to-one-newsletter-this-one-is-called-image-generation-935bcaf0f37c
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Image Generation - Google DeepMind paper with TensorFlow - Deep Learning: Zero to One
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