One-Shot Training of Neural Networks Using Hypercube-Based Topological Coverings
arxiv.org
arxiv.org
To be honest, I can't say I know enough about the math here to do anything more than vaguely follow their explanations despite my ML/NLP background. I'm curious - other ML researchers out there, how much of this are you able to understand? My impression is that this math is pretty far beyond what ML folks typically know, although I'm on the lower end of the spectrum as far as math knowledge goes, so I may be totally wrong (and need to spend more time reading textbooks haha). I wonder if the complexity may slow down progress if it does turn out that this kind of geometric construction can compete with iterative training. It sounds like this approach could potentially support more complex networks by working more on the geometric representation, so I certainly hope this paper serves its purpose of motivating people with the right skillsets to do further exploration.
Imagine the case where an input dataset has three dimensions: each piece of input data could be represented as a point in 3D space. We want to classify each point with a category. Imagine a cube containing all such points, and imagine the cube was subdivided into many tiny cubes of the same size. If these tiny cubes were sufficiently small, then each cube would only contain points belonging to a single category (sufficiently small cubes would only contain a single point, making this trivially true). We could then assign to each cube a category based on the category of the points in it, then if we wanted to classify a new point, we'd just check what cube it's inside and look up the category of that cube.
The above process doesn't scale well (would be too slow in practice). An alternative is, rather than subdividing the cube into tiny evenly-sized cubes, we use an iterative process, starting with the whole cube, finding an ideal dimension along which to split it, splitting it (subject to the constraint that splitting it wouldn't make the children too small), then repeating this process with the two 3D rectangles that resulted from splitting the original shape. This could produce extremely long, thin 3D rectangles however, which might not generalise well to new data, so we cap the aspect ratio of the child rectangles so they can't get too thin. This approach may leave some areas of the original cube unclassified, so for each such area we find the category of the 3D rectangle that the unclassified areas touches most, and assign its category to the unclassified area, then repeat until there are no unclassified areas remaining. The end result approximates the result of subdividing the cube into tiny cubes, but is significantly faster to produce.
The above approach is roughly what's described in the paper, except they describe an n-dimensional hypercube rather than just a 3-dimensional one, and provide more detail on how to determine the ideal axis along which to split a hyperrectangle into child hyperrectangles.
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
X, y = fetch_openml('mnist_784', version=1, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y,
stratify=y,
random_state=1729,
test_size=X.shape[0] - (10 * 20))
model = MLPClassifier(random_state=1729)
model.fit(X_train, y_train)
p = model.predict(X_test)
print(accuracy_score(y_test, p))
X_train, X_test, y_train, y_test = train_test_split(X, y,
stratify=y,
random_state=1729,
test_size=X.shape[0] - (10 * 200))
model = MLPClassifier(random_state=1729)
model.fit(X_train, y_train)
p = model.predict(X_test)
print(accuracy_score(y_test, p))
Personally, my doubts are more in line with the other ones that were raised here, namely tdj's comment on not building on existing literature (the feeling of "this just looks too much like a classification tree" is very strong while reading), and scalability issues. I also tried something vaguely similar in the past (but using iterated linear discriminant analysis instead of hypercubes / classification trees, and convolutional networks instead of fully connected), but never even finished the prototype because it was so terribly slow even on MNIST that nobody in their sane mind would have used it (I could only load into RAM a few samples at a time, which made it messy).
In any case, it's a pity they didn't try to use these weights as initialization for the "usual" backpropagation, it might have lead to interesting results (especially if in doing so they extended to the whole of the input and not just the first 10 PCA - or at least on ore than 10).
Instead of PCA on all features, they could subsample 10 random features to partition, and bag the results of multiple runs. Basically, Totally Random Trees with an arbitrarily handicapped Random Subspaces method. Scales well, can beat Logistic Regression, but not any of the more developed tree methods.
Another difference with established literature is that this algorithm does not use any kind of knowledge transfer from previously learned classes. In most one-shot methods, including those used by humans, the model is already trained on other classes, and uses this information to adapt to unseen classes. Instead, the authors interpret it solely as deep learning under a small training data setting (which -- my code shows -- does not require jumping through hoops).
Reading this I got several dejavus to my grad school classes on classical ML stuff. I like the direction but it feels like it could be better if it admitted that it's a variant of decision tree embedding, and built on some of the massive amount of research work in that area. At least in terms of understanding.
I suspect doing a random forest version of this would actually help. Perhaps we will see this as a legit pre-training step.
Also called Perfect Decision Tree.
CIFAR-10 is the new MNIST today.