Questions tagged [performance]

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

20 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
2
votes
0answers
24 views

What is human-level performance for semantic segmentation?

I see so many papers claim to have an algorithm that beats 'human-level performance' for semantic segmentation tasks, but I can't find any papers reporting on what the human-level performance actually ...
2
votes
0answers
30 views

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. ...
2
votes
0answers
17 views

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 ...
2
votes
0answers
40 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 ...
1
vote
0answers
60 views

Is the performance of a neural network, which was trained with encrypted data and weights, affected if the weights are decrypted?

Suppose that a neural network is trained with encrypted (for example, with homomorphic encryption and, more precisely, with the Paillier partial scheme) data. Moreover, suppose that it is also trained ...
1
vote
0answers
35 views

How should I change the hyper-parameters of the C51 algorithm, in order to obtain higher reward?

I have a scenario where, in an ideal situation, the greedy approach is the best, but when non-idealities are introduced which can be learned, DQN starts doing better. So, after checking what DQN ...
1
vote
0answers
23 views

Would performance of atomic models matter in ensemble methods?

Suppose I have two fitted ensemble models $F_1 := (f_1, f_2, f_3, \cdots f_n)$ and $G_1 := (g_1, g_2, g_3, \cdots g_n)$. And they were using the same ensemble methods (boosting or bagging). And I am ...
1
vote
0answers
23 views

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 ...
1
vote
0answers
37 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 ...
1
vote
0answers
37 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 ...
1
vote
0answers
23 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?
1
vote
0answers
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 ...
1
vote
0answers
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 ...
1
vote
0answers
32 views

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 ...
0
votes
0answers
22 views

Identifying if a model is over or under-fitting via graphs

I am working on a Neural Network and have plotted the performance of my model. However the plots seem not to fit the "trends" (which help you identify the issue with your model) presented in ...
0
votes
0answers
33 views

What is the difference between learning agents and other kinds of agents, and more specifically, between performance standard and performance measure?

I am reading AI: A Modern Approach. In the 2nd chapter when introducing different agent types, i.e., reflex, utility-based, goal-based, and learning agents, I understood that all types of agents, ...
0
votes
1answer
33 views

Can the attention mechanism improve the performance in the case of short sequences?

I am aware that the attention mechanism can be used to deal with long sequences, where problems related to gradient vanishing and, more generally, representing effectively the whole sequence arise. ...
0
votes
0answers
75 views

How to calculate the performance measure of a vacuum cleaner agent?

A simple vacuum cleaner agent is having two sensors. One sensor senses the current location of the agent (floor A or floor B). The other sensor senses the status of the current location (Dirty or ...
0
votes
0answers
18 views

Calculating processing time of a deep learning model

My model deals with videos, and I want to calculate how fast it can process frames as in frames per second or processing time for 1 frame. I have made a single function to get predictions, it takes in ...
0
votes
0answers
75 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 ...