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0 votes

Is there a state-of-the-art deep learning paper that uses center point regression instead of bounding box regression, for object tracking?

I know that some bounding box models like yolo has output of 4 channels: hight-center, width-center,hight-size, width. and with they constract the bounding box, so you can modify those models to use ...
0 votes

How are CNN kernels trained when using FFT for convolutions?

I had that same exact question when trying to develop some kind of implementation of the Conv2D using the FFT. To do this we need to consider that there are three steps that the layer needs to be able ...
2 votes

Are CNNs exactly translation invariant with global max/average pooling layers?

Your intuition is correct, when you apply many local feature detectors (convolutions) and throw the results into one big pot (any global pooling), only the amount of detected features matters, not ...
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0 votes

How vision models based on CNNs learn the relative positions of each pixel in the image?

A filter value in a smaller deeper layer is in a fixed relationship to the geometry of the input. Its position in the filter defines its relation to an image. But positional encoding is actually ...
0 votes

Why do the training and validation loss curves diverge?

You have to keep the training loss value constant. Beyond that, your teaching technique or network model is not suitable for the job. I recommend that you are using MLP.
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8 votes

Will a CNN that is Group Equivariant always be better than a regular CNN?

No, it will very much depend on the specific domain that you're applying the model to, whether or not this type of prior will be good or bad. The same applies to any other prior structure/knowledge ...
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0 votes

why by adding additional information as number of sequence on dataset can avoid overfitting

You are doing a technique called "feature engineering", where we manually add new features to the dataset that tell something about the data, so that the model is able to learn better. Every ...
0 votes

How do you pass from the 192 depth in the first tensor to the 256 in the second tensor?

I suppose that 3x3x192 in Conv. Layer refers to "Kernel" size - you may think of it as a "scanner" that scans through an input tensor. This ...

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