60 votes
Accepted

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

First up, those images (even the first few) aren't complete trash despite being junk to humans; they're actually finely tuned with various advanced techniques, including another neural network. The ...
  • 2,569
28 votes

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

The images that you provided may be unrecognizable for us. They are actually the images that we recognize but evolved using the Sferes evolutionary framework. While these images are almost impossible ...
  • 1,032
19 votes
Accepted

What is a fully convolution network?

Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations. Equivalently, an FCN is a CNN without fully ...
  • 35k
16 votes

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

All answers here are great, but, for some reason, nothing has been said so far on why this effect should not surprise you. I'll fill the blank. Let me start with one requirement that is absolutely ...
  • 1,897
11 votes

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

An important question that does not yet have a satisfactory answer in neural network research is how DNNs come up with the predictions they offer. DNNs effectively work (though not exactly) by ...
  • 1,134
8 votes
Accepted

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

This problem is called object detection. If you have a trainings set of images with boxed objects you can just train a neural network to directly predict the box. I.e. the output has the same ...
8 votes
Accepted

Why does nobody use decision trees for visual question answering?

For vision tasks, neural network models almost always include a number of layers that pool and convolute. The convolutions, in particular, are very useful - they can make the model generalize better ...
8 votes
Accepted

Is it okay to use publicly available Instagram videos to train an AI?

Under US copyright law, this is probably fair use ...but beware of memorization. You may run into more trouble if the AI outputs things very similar to the original work. Also, consult a lawyer to ...
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7 votes
Accepted

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

The similarity of artificial neural networks and the human visual cortex goes very deep, and in many ways the human visual cortex was the inspiration for the techniques we use for the design and ...
6 votes
Accepted

Do deep learning algorithms represent ensemble-based methods?

You should think of them as different approaches. A deep neural net is a single independent model, whereas ensemble models are ensembles of many independent models. The primary connection between the ...
6 votes

What do the words "coarse" and "fine" mean in the context of computer vision?

tl;dr What does that mean in the context of this paper? With "coarse segmentation" the author means a segmentation that doesn't have much detail. "Fine segmentation", on the other hand, refers to ...
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6 votes
Accepted

How do you find the homography matrix given 4 points in both images?

To understand homographies and how to find them, you will need a good dose of projective geometry. I will briefly describe some preliminary concepts that you need to know before trying to find the ...
  • 35k
6 votes
Accepted

How to calculate the distance between the camera and an object using Computer Vision?

In general, calculation of distance between camera and object is impossible if you don't have further scene dependent information. To my knowledge you have 3 options: Stereo Vision If you have 2 ...
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6 votes
Accepted

In Computer Vision, what is the difference between a transformer and attention?

The original transformer is a feedforward neural network (FFNN)-based architecture that makes use of an attention mechanism. So, this is the difference: an attention mechanism (in particular, a self-...
  • 35k
5 votes

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

How is it possible that deep neural networks are so easily fooled? Deep neural networks are easily fooled by giving high confidence predictions for unrecognizable images. How is this possible? Can you ...
  • 442
5 votes

Why is no activation function used at the final layer of super-resolution models?

I am not into the field of super-resolution, but I think this question applies to general neural network construction. Usually, you try to solve a classification problem or a regression problem with ...
5 votes
Accepted

Untrained CNNs as feature extractors?

Yes, it has been demonstrated that the main factor for CNNs to work is its architecture, which exploits locality during the feature extraction. A CNN with random weights will do a random partition of ...
  • 491
5 votes

What could an oscillating training loss curve represent?

Try lowering the learning rate. Such a loss curve can be indicative of a high learning rate. Due to a high learning rate the algorithm can take large steps in the direction of the gradient and miss ...
5 votes

What could an oscillating training loss curve represent?

Overview As it has already been observed, your main problem, beside the training related issues like fixing the learning rate, is you have basically no chance to learn such a big model woth such a ...
5 votes
Accepted

What are the main algorithms used in computer vision?

There are many computer vision (CV) algorithms and models that are used for different purposes. So, of course, I cannot list all of them, but I can enumerate some of them based on my experience and ...
  • 35k
5 votes
Accepted

Formal definition of the Object Detection problem

This is just an idea Given a set of pixels, the task is to decide: Which pixel is the center of an object? What is the size of the bounding boxes with the center is the pixel in part 1? Formula, ...
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4 votes
Accepted

Applications of CNN for detecting crime from video surveillance cameras

After a bit of research I found something kind of close: Artificially intelligent security cameras are spotting crimes before they happen New surveillance cameras will use computer eyes to find 'pre ...
4 votes
Accepted

How does Pinterest decipher what's on unmarked pictures and categorize them?

One of the Pinterest's white paper about Human Curation and Convnets powering item-to-item recommendationsarxiv describes implementation of convolutional neural network (CNN) based visual features (...
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4 votes

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

Can't comment(due to that required 50 rep), but I wanted to make a response to Vishnu JK and the OP. I think you guys are skipping the fact that the neural network only really is saying truly from a ...
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4 votes

Is it possible to train a neural network to estimate a vehicle's length?

Yes! This most certainly can be done. Since you have a labeled dataset, that makes it all the more simple! I would take a look at this project and that should get you where you need to go. The ...
4 votes

Algorithms for scene rotation

If deep learning is what you are trying to use here, you should keep in mind that the real intent behind deep learning is to learn a probability distribution, which means that if you were to use a ...
4 votes

Can YOLO detect large objects?

The example given based on the yolov1 paper: The last layer has a tensor of the dimension 7x7x30. but the dimension of the last tensor is not in every case 7x7x30. let be: S: the number of grid ...
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4 votes
Accepted

Is the word "pose" used correctly in the paper "Matrix Capsules with EM Routing"?

Great question, and one that I think we could have done a better job of answering in the paper. Essentially, the pose matrix of each capsule is set up so that it could learn to represent the affine ...
4 votes

Are there any better visual models for transfer rather than ImageNet?

Why is ImageNet so popular for transfer learning? Models pre-trained on the ImageNet datasets have been the de-facto choice for many years now. Many popular reasons as to why people think that ...
  • 3,113
4 votes

How does the region proposal method work in Fast R-CNN?

Yes, it is not specified because the region proposal algorithm did not change from R-CNN (the previous version from Fast R-CNN, however, in the next verion, Faster R-CNN, this algorithm is replaced by ...
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