Jacob Prince-Bieker
Jacob Prince-Bieker
https://github.com/adobe/antialiased-cnns Since normal convolutional, pooling, etc. layers ignore the Nyquist sampling theorem, they can be very sensitive to slight changes in input. This adds an extra layer that can fix...
Various ones to try include https://github.com/thuml/predrnn-pytorch and ConvLSTMs, and potentially GANs as shown in https://arxiv.org/abs/2104.00954
This is where, with some random chance, we give the RNN like ConvLSTM the ground truth label when its generating sequences in training. This can help with convergenc especially in...
## Detailed Description We want a model that can predict both satellite imagery and pv yield. ## Context While we ultimately just care about the PV output of the model,...
Just MAE or MSE or most normal performance metrics might show that optical flow performs better on average than an ML based approach, even if the ML model outperforms the...
For other nowcasting applications, such as precipitation, the training data is usually sampled so that rainfall exists or is above some threshold in every, or nearly every input sample. This...
PyTorch recently came out with this https://github.com/Distributed-AI/PipeTransformer that sped up training transformers and potentially using less GPU resources, or at least train faster.
Add creating video visualizations with the output from SatFlow models, possibly overlaying the cloud mask as well. The main idea is for the HuggingFace Spaces visualization (https://huggingface.co/spaces/openclimatefix/MetNet) so that we...
As mention in #85 one pre-training idea is to create a flow dataset to pre-train on using clouds. We would need simulated flow, and would want to have realistic clouds...
Relates to #47 Adds averaging the last few flows for predictions, and plotting code