Questions tagged [convolutional-neural-networks]

For questions about convolutional neural networks, also known as CNN or ConvNet.

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Training neural network for good taste in art

I'm a newbie in machine learning, so excuse me in advance). I have an idea to make NN that can estimate visual pleasantness of arbitrary image. Like you have a bunch of images that you like, you train ...
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2answers
8k views

Concatenate convolution layers with different strides in tensorflow.

I am trying to do an inception layer, but it only works if the convolution strides, pool strides and pool size are the same, otherwise I get an error in tf.concat that Dimesion 1 is not the same. ...
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0answers
64 views

Can a CNN or MLP discover similar but untrained-on patterns?

I've been experimenting with a simple tic-tac-toe game to learn neural network programming (MLP and CNNs) with good results. I train the networks on a board positions and the best moves and the ...
6
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1answer
275 views

Can a purely policy convolution neural network based game learn to play better than its opponents?

This question has come from my experiment of building a cnn based tic-tac-toe game that I'm using as a beginner machine learning project. The game works purely on policy networks, more specifically - ...
9
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1answer
355 views

How much of a problem is white noise for the real-world usage of a DNN?

I read that deep neural networks can be relatively easily fooled (link) to give high confidence in recognition of synthetic/artificial images that are completely (or at least mostly) out of the ...
5
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1answer
785 views

How does visual cortex share convolution weight

TL;DR If we buy into the idea visual cortex functions like a convolutional neural network, then there's a problem makes me scratch my head: how does brain force weight sharing as in convolutional ...
5
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1answer
122 views

Use ConvNet to predict bitmap

I want to build a classifier which takes an aerial image and outputs a bitmap. The bitmap is supposed to be 1 at every pixel where the aerial image has water. For this process I want to use a ConvNet ...
5
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2answers
366 views

Feasibility of generating large images with a convnet

I've spent the past couple of months learning about neural networks, and am thinking of projects that would be fun to work on to cement my understanding of this tech. One thing that came to mind last ...
4
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1answer
2k views

Is the QuickDraw with Google neural net a convolutional neural network?

Does anyone know, or can we deduce or infer with high probability from its characteristics, whether the neural network used on this site https://quickdraw.withgoogle.com/ is a type of convolutional ...
3
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1answer
123 views

What linear rectifier is better?

What rectifier is better in general case of Convolutional Neural Network and how about empirical rules to use each type? ReLU PReLU RReLU ELU Leacky ReLU
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5answers
4k views

CNN for detecting not just the nature of the object, but position within image as well

Consider a typical convolutional neural network like this example that recognizes 10 different kinds of objects from the CIFAR-10 dataset: https://github.com/tflearn/tflearn/blob/master/examples/...
6
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4answers
4k views

What is the purpose of hidden nodes in neural network?

If I have a set of sensory nodes taking in information and a set of "action nodes" which determine the behavior of my robot, why do I need hidden nodes between them when I can let all sensory nodes ...
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3answers
4k views

When using neural networks to detect features in an image, how can locate that specific feature in the original image?

I understand how a neural network can be trained to recognise certain features in an image (faces, cars, ...), where the inputs are the image's pixels, and the output is a set of boolean values ...
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3answers
52k views

How can neural networks deal with varying input sizes?

As far as I can tell, neural networks have a fixed number of neurons in the input layer. If neural networks are used in a context like NLP, sentences or blocks of text of varying sizes are fed to a ...
5
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3answers
2k views

Are Convolutional Neural Networks better than existing image recognition libraries that don't use CNNs?

Are Convolutional Neural Networks summarily better than pattern recognition in all existing image processing libraries that don't use CNN's? Or are there still hard outstanding problems in image ...
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2answers
1k views

Do deep learning algorithms represent ensemble-based methods?

According to the Wikipedia article on deep learning: Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep ...
2
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1answer
328 views

How does deepmind's Atari game AI work?

I know that deepmind used deep Q learning (DQN) for its Atari game AI. It used a conv neural network (CNN) to approximate Q(s,a) from pixels instead of from a Q-...
4
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1answer
937 views

Applications of CNN for detecting crime from video surveillance cameras

Inspired by this discussion about recognizing human actions, I have found the Fall-Detection project which detects humans falling on the ground from a CCTV camera feed, and which can consider alerting ...
5
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3answers
213 views

How to make convnets aware what the image actually is, not what is depicted on it?

I've uploaded a picture to Wolfram's ImageIdentify of graffiti on the wall, but it recognized it as 'monocle'. Secondary guesses were 'primate', 'hominid', and 'person', so not even close to 'graffiti'...
4
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1answer
200 views

Is it possible to fool a facial recognition algorithm?

Assuming we're dealing with an artificial neural network (e.g. a ConvNet), which was trained by a large dataset of human faces. Are there any known issues or challenges where facial recognition would ...
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2answers
108 views

How to separate image recognition from logic?

For example I would like to implement transparent AI in the RTS game which doesn't offer any AI API (like old games), and I'd like to use image recognition algorithm for detecting the objects which ...
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7answers
16k views

Do scientists know what is happening inside artificial neural networks?

Do scientists or research experts know from the kitchen what is happening inside complex "deep" neural network with at least millions of connections firing at an instant? Do they understand the ...
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2answers
2k views

How successfully can convnets detect NSFW images?

For example, search engine companies want to classify their image searches into 2 categories (which they already do that) such as: NSFW (nudity, porn, brutality) and safe to view pictures. How can ...
2
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1answer
438 views

Can ConvNets be used for real-time object recognition from video feed?

Convolutional neural network are leading type of feed-forward artificial neural network for image recognition. Can they be used for real-time image recognition for videos (frame by frame), or it takes ...
4
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1answer
175 views

Why would neural network dream scenes mirror the hallucinations people experience when they're tripping?

In DeepDream wikipedia page it's suggested that a dreamlike images created by a convolutional neural network may be related to how visual cortex works in humans when they're tripping. The imagery ...
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1answer
492 views

Is it possible to apply deep dream technique for the audio streams?

What happens if you apply the same deep dream technique which produces "dream" visuals but to media streams such as audio files? Does changing image functions into audio and enhancing the logic would ...
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0answers
49 views

Can DeepDream produce a "dream" from 3 images?

By default using DeepDream technique you can creating a dreamlike image out of two different images. Is it possible to easily enhance this technique to generate one image out from three?
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9answers
6k views

How is it possible that deep neural networks are so easily fooled?

The following page/study demonstrates that the deep neural networks are easily fooled by giving high confidence predictions for unrecognisable images, e.g. How this is possible? Can you please ...
24
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4answers
409 views

Is the pattern recognition capability of CNNs limited to image processing?

Can a Convolutional Neural Network be used for pattern recognition in problem domains without image data? For example, by representing abstract data in an image-like format with spatial relations? ...

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