I want to use a LSTM to generate images, I have inputs images of (30,2,32,32) and target images of (30,1,32,32), How should I structure my data such I will be able generate images using the following architecture ? Or is it even possible to use this simple architecture to get reasonable results?
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To generate images I would use a GAN over a LSTM.
Take a look at these GANs for image Generation.
The image I’m generating are a sequence, that is why I’m using an LSTM, but I will have a look the GAN repo thanks.
GAN framework may be used with almost any kind of neural networks. Take a look at this papers for example:
- Continuous recurrent neural networks with adversarial training https://arxiv.org/abs/1611.09904
- Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs https://arxiv.org/abs/1706.02633
- Language Generation with Recurrent Generative Adversarial Networks without Pre-training https://arxiv.org/abs/1706.01399
- Contextual RNN-GANs for Abstract Reasoning Diagram Generation https://arxiv.org/abs/1609.09444
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First, it works for sure.
Second, even though I’m unsure of this process, I think you can view the image generating lstm as the decoder lstm from Seq2Seq from NLP. https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html#sphx-glr-intermediate-seq2seq-translation-tutorial-py Your current approach might work as well; you can test the result different approaches.
Third, you don’t have to initialize the hidden states to zeros manually, Pytorch lstm does that automatically.
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Thank you very much for your help.
What do you mean by " it works for sure" you mean for my specified problem or it work in general?
Generating an image step by step using an lstm can be generalized to a sequence generating problem, and lstm is a common choice for generating sequences.
How would you configure your data if you had a inputs data set with the following dimension (batch_size=256, channels=2, height=64, width=64) to match the LSTM input format (sequence_length, batch_size, input_size) ?
You would first have to convert the image into a series of data; for example, flatten the image in some way(or flatten it using Unfold). Then reshape the tensor into the shape that match the input.