I’m currently encountering an issue while using U-Net. My input consists of multi-channel grid data derived from various meteorological features, and the output is a 7-hour future rainfall prediction map. Each grid point is represented as either 1 or 0, indicating whether the accumulated rainfall in that grid exceeds 40 mm.
Initially, I planned to train 7 separate models, each responsible for predicting rainfall at a specific future time step. However, ChatGPT suggested an alternative approach: using a single model with 7 output channels, where each channel corresponds to a different future time step.
This leads me to a question.
U-Net is typically used for semantic segmentation, where multiple channels represent different semantic categories.
Does this approach make sense, or would training separate models be more effective? I’d appreciate any insights!
Well… From my point of view, training a single model should be a better choice in most cases…
You could interpret your model as two parts: encoder (i.e. all layers except for the last layer) and classifier (i.e. the last layer). Training a single model is somehow equivalent to training a shared encoder and 7 separated classifiers ---- less parameters (i.e. no need for 7 encoders), more parallelization (i.e. one forward pass for all, not 7 forward passes).
Moreover, generally speaking, you cannot assume your future predictions are not correlated to each other. With a shared encoder, your model could capture correlations between different future predictions (i.e. features that are useful for all predictions). If you could safely assume that all future predictions are independent, you could, in theory, achieve similar or the same accuracy with separated models, but generally speaking, I would recommend starting with a single model.
P.S.
Experiments are always recommended. If you are uncertain and you have enough time, get your hands dirty and try it
I think your consideration of the correlation between future time steps is very reasonable, and this is something I hadn’t deeply thought about before. However, I still have some doubts and would like to further explore the applicability of this approach.
Generally, in semantic segmentation, the multi-channel output of U-Net is primarily used for multi-class classification, where each pixel belongs to only one class, and the output channels typically represent the probability of the pixel belonging to each class. For example, in a standard semantic segmentation task with C classes, U-Net’s output is usually in the shape of (batch, C, H, W), where each channel corresponds to the probability of a pixel belonging to a specific class.
However, in my case, each pixel’s label is not a mutually exclusive multi-class classification, but rather 7 independent binary classification tasks. This makes me a bit confused about whether this approach aligns with the intended use of U-Net.
It is true that U-Net was originally developed for (biological) semantic segmentation,
where each pixel has a binary or multi-class classification predicted for it. However,
the basic U-Net concept and architecture is not limited to predicting classes. For
example, you could well train a U-Net on a per-pixel regression task.
(The basic concept of U-Net is that it makes per-pixel predictions that, because it is
fully convolutional, depend – for a given pixel – only on a subregion of the input image
around the corresponding input pixel. This is called the U-Net’s “field of view.”)
I am assuming that your “prediction map” is an “image” of the same size as your
“multi-channel grid data” input “image.” Your output could then have seven channels,
one for each hour, with each channel consisting of a per-pixel logit that predicted
whether that pixel would exceed 40 mm of rainfall. This would be a binary classification
task (with multiple pixels and channels).
You could, instead (assuming that you have ground-truth training data that consist of
actual rainfall amounts, not just thresholded values), perform a (per-pixel, per-channel)
regression task where you predict the actual amount of rainfall you expect as a continuous
value (for a particular grid point during a particular hour).
I could imagine that you would get better results training a regression model on such
unthresholded data, because thresholding the data and then training on it would be
throwing away potentially useful information.
U-Net is perfectly appropriate for performing such (per-pixel) regression tasks.
(As an aside, for the reasons @Naming-isDifficult gave, I would certainly not train
separate models – just train one model that makes seven, highly related predictions.)
So yeah, many thanks to @KFrank for the well-structured answer. I would just post some points I was just editing.
Short Answer: Stable Diffusion also uses U-Net, but instead of segmentation, U-Net is predicting noises.
Okay, I would now try to explain what is happening here.
You might have the impression that each type of neural network is designed only for specific tasks — like U-Net for segmentation and ResNet for classification. However, this is not the case. In fact, modern neural networks are based on a concept called the Universal Approximation Theorem. In essence, this theorem suggests that with enough parameters, any function can be approximated by a simple three-layer MLP. (Yes, if you are crazy enough, you could even make a three-layer MLP out-perform ChatGPT, in theory.)
From my point of view, model structures (ResNet, U-Net, Transformer, etc.) determine the “search space” of your model’s parameters. A well-chosen architecture gives you a cleaner, more direct search space, making it easier to train the model ---- sometimes your model might refuse to converge if your search space is trash.
I believe that’s all what I want to comment. @KFrank has already answered your question in detail, which I was editing just now
