I trained a simple model to recognize handwritten numbers from the mnist dataset. Here it is:
model = Sequential([ Conv2D(filters=1, kernel_size=(3,1), padding='valid', strides=1, input_shape=(28, 28, 1)), Flatten(), Dense(10, activation='softmax')])
I experimented with varying the number of filters for the convolutional layer, while keeping other parameters constant(learning rate=0.0001, number of episodes=2000, training batch size=512). I used 1, 2, 4, 8, and 16 filters, and the model accuracy was 92-93% for each of them.
From my understanding, during the training the filters may learn to recognize various types of edges in the image (e.g, vertical, horizontal, round). This experiment made me wonder whether any of the filters end up being duplicate -- having the same or similar weights. Is there anything that prevents them from that?