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From mathematical point of view you are correct as are your calculations. To catch all the patterns you need that many filters, but this is where a whole idea of a training comes in. Main objective of the training in the CNNs is to find just a few good patterns from billions possible ones. So the direct answer to your question is: The standard layers of 64 ...

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In addition in general it somewhat aides in detection as only the strongest feature feature filter is activated so in a sense it removes additional information. But it obviously has draw backs resulting in combinations of features being detected which aren't actual.objects.

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Pooling has multiple benefits Robust feature detection. Makes it computationally feasible to have deeper CNNs Robust Feature Detection Think of max-pooling (most popular) for understanding this. Consider a 2*2 box/unit in one layer which is mapped to only 1 box/unit in the next layer (Basically pooling). Let's say the feature map (kernel) detects a petal ...

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My immediate suggestion would be to zero-fill the missing values, but I recalled the below comment suggesting a more sophisticated method: Karim: How to deal with different size of feature vectors? Nabila: That's a problem I'm actually working on. I've seen that you can create separate networks for each type of node feature, and sort of project them -...

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From what I understand, don't bother with a CNN, you have essentially perfectly structured images. You can hand code detectors to measure how much filled in a circle is. Basically do template alignment and then search over the circles. Ex a simple detector would measure the average blackness of the circle which you could then threshold.

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While this may not be the answer you were looking for, I hope this explanation will help you to understand applying backpropagation to a CNN. Fundamentally, convolutional layers are no different than dense layers, however there are restrictions. The key one is weight-sharing which allows a CNN to be much more efficient than a regular dense layer (as well as ...

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Why are CNNs useful? The main property of CNNs that make them more suitable than FFNNs to solve tasks where the inputs are images is that they perform convolutions (or cross-correlations). Convolution The convolution is an operation (more precisely, a linear operator) that takes two functions $f$ and $h$ and produces another function $g$. It's often ...

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Quantization is a technique used to make deep learning models smaller and faster to run. Deep learning models are essentially collections of real-valued numbers. Because there are infinitely many real numbers, computers represent them using a format call 'floating point' numbers, which are not completely accurate. For example, a 32-bit floating point ...

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So there's pictures of low level activation maps, and some gradient based information where yoy take the deriviative of the output with respect to the input and generate a heatmap. I kind of have my doubts on how usefull this is in general, imo it kind of is creating a fallacious illusion of understanding. There's some additional research using blurring to ...

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The fact that features are always positive values don't guarantee that outputs of hidden layers are positive too. Due to multiplication, output of an hidden layer could contain negative values, i.e., a hidden layer can contain weights that have opposites signs as its input. Remember that only layer outputs, not their weights, are passed through ReLu, so, ...

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While it would certainly help if the link to the paper could also be posted, I will give it a shot based on what I understand from this picture. 1) For any convolutional layer, there are few important things to configure, namely, the kernel (or, filter) size, number of kernels, stride. Padding is also important but it is generally defined to be zero unless ...

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Yes, it is applied element-wise on every single value of the feature map. Assuming ReLU as your nonlinearity function, all negative values of the image feature map are set to zero, and the rest of the elements stay unchanged.

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This is not an uncommon situation. The data set your model is trained on represents a certain probability distribution. Your test set is most likely a good representation from that distribution so your test results will be good. However when you use real world images they may or may not have a similar distribution. Typically if the training set is large and ...

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I’m assuming that you used LeNet (our some other model with small number of parameters) since your training image size is 28x28. Note that LeNet doesn’t generalize well to new images. I think it performs fine (>90%) on MNIST but not good on CIFAR10 (>60%) albeit both datasets contain similar size image. (Just trying to remember the performance from PyTorch ...

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First and foremost, I have to say that this could (and likely will) be a very hard task. Neural networks (NNs) have excelled at computer vision tasks identifying everything from textures to complex objects but what you are trying to do goes beyond that. We (humans) identify trash using the context as much as the object. An object on a table and the same ...

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