Questions tagged [cnn]
Convolutional Neural Networks (CNN, also called ConvNets) are a tool used for classification tasks and image recognition. The name giving first step is the extraction of features from the input data.
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How to update bias in CNN?
How do we find the gradient and the back propagation error if we had a bias which just added a scalar value after the convolution calculation?
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Tool for designing CNN architectures
I am currently building a (simple sequential) fully convolutional network for an object recognition task.
Designing the architecture essentially amounts to choosing kernel size, stride and unit ...
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Can I train two stacked models end-to-end on different resolutions?
Is it possible to stack two networks on top of each other that operate on different resolutions of input data?
So here's my usecase: like Google, I want to recognize text in images. Unlike Google, I ...
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Should the bias value be added after convolution operation in CNNs?
Should we add bias to each entry of the convolution then sum, or add bias once at end of calculating the convolution in CNNs?
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CNN Multi-class vs Binary Class Image Classification
Suppose we have a training set of 3 classes of image: 1.Cats, 2.Dogs, 3.Neither cats nor dogs.
We're only really bothered about detecting whether an image is a cat/dog, or neither, but we don't care ...
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Understand clearly the figure: Illustration of a Convolutional Neural Network (CNN) architecture for sentence classification
I am studying the blog: Understanding Convolutional Neural Networks for NLP. It is very good blog.
One thing I can't understand clearly about this blog. As the figure Illustration of a Convolutional ...
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What are differentiable modules used in deep learning
I am reading this paper.
Convolutional Neural Networks define an exceptionally powerful class of models,
but are still limited by the lack of ability to be spatially invariant to the input data
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What pre processing should I use on data to feed into a CNN?
I have a dataset of shape 105 x 501 x 266 where 105 is the number of data and 501 x 266 is ...
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Relation between convolution in math and CNN
I've read explanation of convolution and understand it to some extent. Can somebody help me understand how this operation relates to convolution in Convolutional Neural Nets? Is filter like function <...
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Are there pretrained models on the ImageNet Bounding Boxes dataset?
When searching for some pretrained models for object detection with bounding boxes, I was wondering if there are also pretrained models on the ImageNet dataset for bounding boxes. Most of the time, I ...
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Fine tuning CNN with imbalance data gives good results - not sure why
I'm fine tuning with caffenet for a 3 classes classification problem. I have 100 instances of class A, 90 instances of class B and 30 instances of class C. I thought that my net would be biased toward ...
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What is preferred upsampling or zero padding?
When training a CNN one option is either to zero pad an image to make it bigger or upsample it. When should I choose each one?
What criteria is leveraged for choosing a method?
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What is the memory cost of a CNN?
I was recently thinking about the memory cost of (a) training a CNN and (b) inference with a CNN. Please note, that I am not talking about the storage (which is simply the number of parameters).
How ...
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Handwritting Recognition moving from character level to word level
Given the experience on MIST, I try this problem as a character level.
I have a handwritten text and I want to "OCR" it. Even though I made progresses with openCV (on the image pre-processing, ...
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Why do convolutional neural networks work?
I have often heard people saying that why convolutional neural networks are still poorly understood. Is it known why convolutional neural networks always end up learning increasingly sophisticated ...
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