Questions tagged [performance]

For question about methods of evaluating the performance (e.g. the accuracy) of an artificial intelligence algorithm or model.

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How is the performance of a CNN trained with monochrome images on image recognition tasks degraded?

For CNN image recognition tasks, like object recognition/face recognition/object segmentation/posture recognition, are there experiment results about how much will the performance be degraded with ...
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1answer
41 views

Which kind of data does sigmoid kernel performance well?

While I was playing with some hyperparameters, I came to a wired situation. My dataset is IRIS dataset to be specific. SVM algorithm has some hyperparameters that we can tune, such as Kernels, and C ...
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35 views

How many training runs are needed to obtain a credible value for performance?

I'm trying to optimize a neural network. For that, I'm changing parameters like the batch size, learning rate, weight initialization, etc. A neural network is not a deterministic algorithm, so, in ...
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4answers
75 views

Is it normal to have the root mean squared error greater on the test dataset than on the training dataset?

I am new to deep learning. I am training a model and I am getting a root mean squared error (RMSE) greater on the test dataset than on the training dataset. What could be the reason behind this? Is ...
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1answer
28 views

How to estimate the accuracy upper limit of any CNN model over a computer vision classification task

We are given a computer vision classification task, that is, a task that asks us to predict the category of an image over $n$ predefined classes (the so-called closed set classification problem). ...
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Can the addition of low-quality images to the training dataset increase the network performance?

I already trained a deep neural network called YOLO (You Only Look Once) with high-quality images (1920 by 1080 pixels) for a detection task. The result for mAP and IOU were 93% and 89% respectively. ...
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1answer
285 views

How can I merge outputs of two separate layers so that the overall performance improves?

I am training a combined model (fine-tuned VGG16 for images and shallow FCN for numerical data) to do a binary classification. However, the overall AUC score is not what I expected it to be. Image-...
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2answers
62 views

Why there's so much search for Python professionals in AI? [closed]

Even with ample knowledge about the low computational performance that Python has, and even though it is an interpreted language that is not recommended for real-time applications, it is the ...
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34 views

Sample size for the evaluation of Deep Learning Models

I'm evaluating the performance and accuracy in detecting objects for my data set using three deep learning algorithms. In total there are 24,085 images. I measure the performance in terms of time ...
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20 views

What are the properties of a model that is well suited for for high performance real-time inference

What are general best practices or considerations in designing a model that is optimized for real-time inference?
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1answer
167 views

Why is the effective branching factor used for measuring performance of a heuristic function?

For search algorithms with heuristic functions, performance of heuristic functions are measured by effective branching factor ${b^*}$ which involves total nodes expanded ${N}$ and depth of the ...
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13 views

Quantization techniques and new GPU architectures [closed]

Quantization means using low resolution formats for some variables some of the time: binary (e.g. BinaryConnect), ternary, etc. The Turing architecture recently introduced by Nvidia is much faster ...
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1answer
57 views

What will change when workstations will have ARM Machine Learning Processors onboard?

lately we read that many manufacturers are forcing ARM architectures to be used on future workstations. One of ARM's recent announcements is a machine learning processor. What will change in terms of ...
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64 views

What to do when an image classifier does good for a class but bad for another?

So I wrote a convolutional neural network for a binary image classification. I have around 5300 images for each class which I thought would be enough to at least give me a good accuracy on the ...
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1answer
44 views

How can we conclude that an optimization algorithm is better than another one

When we test a new optimization algorithm, what the process that we need to do?For example, do we need to run the algorithm several times, and pick a best performance,i.e., in terms of accuracy, f1 ...
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21 views

Improving Recall of a Certain Class

Let's say that we have a test data set with $20,000$ observations for which we want to make a binary prediction for. When we apply our best trained model to this data set (e.g. logistic regression ...
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In addition to matrix algebra, can GPU's also handle the various Kernel functions for Neural Networks?

I've read a number of articles on how GPUs can speed up matrix algebra calculations, but I'm wondering how calculations are performed when one uses various kernel functions in a neural network. If ...
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31 views

Why does precision-recall curve become more stable when neural net begins to overfit?

I am training a convLSTM with a dropout layer (with prob 0.5). If I train over more than 5 epochs I notice that the network starts to overfit: my validation set loss becomes stationary while the ...
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2answers
121 views

How do I improve accuracy and know when to stop training?

I am training a modified VGG-16 to classify crowd density (empty, low, moderate, high). 2 dropout layers were added at the end on the network each one after one of the last 2 FC layers. network ...
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1answer
89 views

What are the aspects that most impact on the inference time for neural networks in embedded systems?

I work with neural networks for real-time image processing on embedded softwares and I tested different architectures (Googlenet, Mobilenet, Resnet, custom networks...) and different hardware ...
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1answer
316 views

Why isn't the ElliotSig activation function widely used?

The Softsign (a.k.a. ElliotSig) activation function is really simple: $$ f(x) = \frac{x}{1+|x|} $$ It is bounded $[-1,1]$, has a first derivative, it is monotonic, and it is computationally ...
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37 views

Performance measure on windowed time series data

I have time series data where I use a sliding window to detect anomalies in those windows. A sliding window is an interval of the dataset that steps one datapoint for each iteration. Datapoints are ...
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1answer
146 views

How can we compare the intelligence of AI systems?

One way of ranking human intelligence is based on IQ tests. But how can we compare the intelligence of AI systems? For example, is there a test that tells me that a spam filter system is more ...
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How to visualize/interpret text prediction model results?

I am using LSTM model to predict the next xml markup from an input seed. I have trained my model on 1500 xml files. Each xml file is generated randomly. I am wondering if there is a way to visualize ...
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1answer
94 views

Machine Learning - Is selected models combination good?

I've selected more than 10 discriminative (Classification) models, each wrapped with a BaggingClassifier object, optimized with a GridSearchCV, and all of them placed within a VotingClassifier object. ...
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1answer
83 views

Relative compute time for each type of layer in a neural network

Hello, I would like to know whether this picture from the paper: Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability valid? Questions: 1) Does ...
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2answers
960 views

Does the quality of training images affect the accuracy of the neural network?

I just got into AI few months ago. I noticed most of the images in training datasets are usually low quality( almost pixelated). Does the quality of training images affect the accuracy of the neural ...
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475 views

What does deep learning offer with respect to standard machine learning?

I've been reading a lot about DL. I can understand to an extent how it works, in theory at least, and how it's technically different from conventional ML. But what I'm looking for is more of a "...
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1answer
399 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 ...
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495 views

Why is the generation of deep style images so slow and resource-hungry?

Consider these neural style algorithms which produce some art work: Neural Doodle neural-style Why is generating such images so slow and why does it take huge amounts of memory? Isn't there any ...
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4answers
2k views

What is the “dropout” technique?

What purpose does the "dropout" method serve and how does it improve the overall performance of the neural network?