Questions tagged [convnet]

For questions regarding "Convolutional Neural Networks" (CNN)

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27
votes
6answers
10k views

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 ...
222
votes
11answers
208k views

What are deconvolutional layers?

I recently read Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, Trevor Darrell. I don't understand what "deconvolutional layers" do / how they work. The ...
12
votes
1answer
3k views

Using a pre trained CNN classifier and apply it on a different image dataset

How would you optimize a pre-trained neural network to apply it to a separate problem? Would you just add more layers to the pre-trained model and test it on your ...
18
votes
1answer
16k views

back propagation in CNN

I have the following CNN: I start with an input image of size 5x5 Then I apply convolution using 2x2 kernel and stride = 1, that produces feature map of size 4x4. Then I apply 2x2 max-pooling with ...
69
votes
4answers
29k views

How are 1x1 convolutions the same as a fully connected layer?

I've recently read Yan LeCuns comment on 1x1 convolutions: In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with 1x1 convolution kernels ...
41
votes
2answers
39k views

How to prepare/augment images for neural network?

I would like to use a neural network for image classification. I'll start with pre-trained CaffeNet and train it for my application. How should I prepare the input images? In this case, all the ...
14
votes
1answer
4k views

Back-propagation through max pooling layers

I have a small sub-question to this question. I understand that when back-propagating through a max pooling layer the gradient is routed back in a way that the neuron in the previous layer which was ...
11
votes
3answers
27k views

Question about bias in Convolutional Networks

I am trying to figure out how many weights and biases are needed for CNN. Say I have a (3, 32, 32)-image and want to apply a (32, 5, 5)-filter. For each feature map I have 5x5 weights, so I should ...
8
votes
4answers
4k views

How does deep learning helps in detecting multiple objects in single image?

Let's say there are two cars in an image. How can it detect these cars, given that it can detect single car in an image?
6
votes
1answer
4k views

Sigmoid vs Relu function in Convnets

The question is simple: is there any advantage in using sigmoid function in a convolutional neural network? Because every website that talks about CNN uses Relu function.
4
votes
2answers
2k views

How to sort numbers using Convolutional Neural Network?

Recently, in an interview I got this question: Design a convnet that sorts numbers. Operators are ReLU, Conv, and Pooling. E.g. input: 5, 3, 6, 2; output: 2, 3, 5, 6 I am not sure how can you sort a ...
37
votes
4answers
19k views

How do subsequent convolution layers work?

This question boils down to "how do convolution layers exactly work. Suppose I have an $n \times m$ greyscale image. So the image has one channel. In the first layer, I apply a $3\times 3$ ...
14
votes
2answers
18k views

How many images per class are sufficient for training a CNN

I'm starting a project where the task is to identify sneaker types from images. I'm currently reading into TensorFlow and Torch implementations. My question is: how many images per class are required ...
4
votes
2answers
149 views

Why are my predictions bad, if my accuracy in train is roughly 100% (Keras CNN)

In my CNN i have to handle 2 classes in a binary system, I have 700 images each class to train, and others to validation. This is my train.py: ...
8
votes
4answers
3k views

Faster-RCNN how anchor work with slider in RPN layer?

I am trying to understand the whole Faster-RCNN, From https://www.quora.com/How-does-the-region-proposal-network-RPN-in-Faster-R-CNN-work Then a sliding window is run spatially on these feature ...
2
votes
1answer
5k views

Deconvolution vs Sub-pixel Convolution

I recently read Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network by Wenzhe Shi et al. I cannot understand the difference between ...
7
votes
1answer
1k views

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 ...
6
votes
4answers
2k views

Why choose TensorFlow?

I have noticed that most of the deep learning developers use TensorFlow. So why choose TensorFlow? What is the advantage of TensorFlow over Theano and CNTK?
5
votes
2answers
3k views

Correct number of biases in CNN

What is the correct number of biases in a simple convolutional layer? The question is well enough discussed, but I'm still not quite sure about that. Say, we have (3, 32, 32)-image and apply a (32, 5,...
5
votes
1answer
737 views

1x1 Convolution. How does the math work?

So I stumbled upon Andrew Ng's course on $1x1$ convolutions. There, he explains that you can use a $1x1x192$ convolution to shrink it. But when I do: ...
4
votes
1answer
12k views

Keras Conv1D for simple data target prediction

I am trying to use conv1D layer from Keras for predicting Species in iris dataset (which has 4 numeric features and one categorical target). Following is my code: ...
4
votes
2answers
6k views

What is the shape of conv3d and conv3d_transpose?

I want to do a GAN with coloured pictures. This means I need a three dimensional input and therefore I like to use conv3d and conv3d_transpose. Unfortunately in the TensorFlow documentation, I can't ...
4
votes
3answers
4k views

Should there be a flat layer in between the conv layers and dense layer in YOLO?

Should there be a flat layer in between the conv layers and dense layer in YOLO? It's something not specified in the paper, but I see most implementations of YOLO on github do this. In my ...
4
votes
1answer
931 views

What is deconvolution operation used in Fully Convolutional Neural Networks?

When I was reading this this paper, Fully Convolutional Networks for Semantic Segmentation, I found that they use an up-sampling layer to classify each pixel in to a class. I have two questions: How ...
4
votes
3answers
5k views

Number of Fully connected layers in standard CNNs

I have a question targeting some basics of CNN. I came across various CNN networks like AlexNet, GoogLeNet and LeNet. I read at a lot of places that AlexNet has 3 Fully Connected layers with 4096, ...
3
votes
1answer
697 views

How to make a region of interest proposal from convolutional feature maps?

Problem Keras does not have any direct implementation of region of interest pooling. I am aware of how to perform maxpooling, but I don't know how to get bounding boxes from feature maps passed from ...
2
votes
2answers
2k views

Training Validation Testing set split for facial expression dataset

I am using Convolutional Neural Networks (CNN) and I just want to ask if the way I split my training/validation/testing set is correct. I have a total of 55 subjects. I plan to split them into 80–10–...
8
votes
1answer
1k views

CNN for phoneme recognition

I am currently studying this paper, in which CNN is applied for phoneme recognition using visual representation of log mel filter banks, and limited weight sharing scheme. The visualisation of log ...
3
votes
1answer
901 views

How to apply my deep learning model to a new dataset?

I am doing semantic segmentation (multi-class classification of image pixels) using convolutional neural networks (CNN) in Keras. In particular, I am applying this to aerial images of crops (...
3
votes
3answers
905 views

What is the state-of-the art ANN architecture for MNIST?

What is actually the best neural network architecture for the classic MNIST digit classifying task? I couldn't find any that would claim to be the winner...
2
votes
2answers
2k views

Can pooling ever increase accuracy in convolutional neural networks?

In ConvNets, pooling is used to downsize the input volume, leading to fewer parameters, leading to computational efficiency and possibly helping with overfitting. But can pooling ever increase the ...
2
votes
2answers
996 views

Data augmentation based on the class type in the CNN model

I would like to use CNN model to classify images but some classes in my dataset have low amount of data. Can I apply data augmentation based on the number of the images in the class? For example, ...
1
vote
1answer
880 views

How to arrange the image dataset in CNN?

How do I arrange the image dataset in CNN? Should I put each image category in a separate folder? Or all of them in the same folder? Should the image name be the category name? I would like to see an ...
1
vote
0answers
87 views

Using neural networks to recognize digits in a scene

Neural nets are widely used (as example for the MNIST dataset). Using neural networks and convolutional neural networks, in particular, we can get over 99% accuracy. However, the MNIST dataset is a ...
1
vote
2answers
124 views

ML model to transform words

I build model that on input have correct word. On output there is possible word written by human (it contain some errors). My training dataset looks that: ...