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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Why does mean episode reward during training differ dramatically from "manual" runs of the trained model on same data?

I am training an RL agent, using PPO, on a time-series environment that comes from a tabular dataset. The possible scores during an episode goes from -1 to positive infinity (though realistically, I ...
3 votes
2 answers
1k views

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

For search algorithms with heuristic functions, the performance of heuristic functions are measured by the effective branching factor ${b^*}$, which involves the total number of nodes expanded ${N}$ ...
4 votes
2 answers
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How do you measure multi-label classification accuracy?

Multi-label assignment is the task in machine learning to assign to each input value a set of categories from a fixed vocabulary where the categories need not be statistically independent, so ...
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1 answer
73 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. ...
1 vote
1 answer
81 views

What could cause the hamming loss and subset accuracy to get stuck in a multi-label image classification problem?

I am rather new to deep learning and got some questions on performing a multi-label image classification task with keras convolutional neural networks. Those are mainly referring to evaluating keras ...
2 votes
1 answer
39 views

What is a 'degenerate run' in evaluating model performance?

I've recently come across a paper that uses the term "degenerate run", but I'm not sure if I understand what it means. The idea is that when they report the average performance of running ...
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28 views

Neural network for an output in the form of a probability distribution

I am not an expert in machine learning. Recently, I want to construct a data-driven model based the neural network. The problem is that I want from my algorithm to learn an output in the form of a ...
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1 answer
51 views

Confusion Matrix Measures vs Accuracy level in Neural Network Model

I'm working on a classification machine learning problem with two classes: high and low, which are derived from another numerical column x. Previously, if x>100, the sample is considered ...
1 vote
1 answer
66 views

Why doesn't the high precision of neural network weights improve accuracy?

Consider the following paragraph from the subsubsection 3.5.2: A dtype for every occasion chapter named It starts with a tensor from the textbook titled Deep Learning with PyTorch by Eli Stevens et al....
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GAN performance starts to get worse as training continues

I'm currently trying to train a GAN to recreate similar images from a dataset. The dataset is using the Eiffel Tower Pictures from Googles Quick Draw dataset. The images aren't very large (only 12x12 ...
11 votes
3 answers
1k views

What size of neural networks can be trained on current consumer grade GPUs? (1060,1070,1080)

Is it possible to give a rule of thumb estimate about the size of neural networks that are trainable on common consumer-grade GPUs? For example, the Emergence of Locomotion (Reinforcement) paper ...
1 vote
1 answer
78 views

Test accuracy decreases during my train process

I want to train a neural network model with the arcface loss function and try to combine it with domain adaption. But when the training process continues, I find the test accuracy first increases and ...
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1 answer
68 views

Do I need to tune the hyper-parameters or more data if SVR model performs poorly?

I am using non-linear data to SVR and have tried tuning the hyperparameters and still have a poor model performance. Do I need more data or format the data for more suitable results? I get similar ...
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Which neural network architecture to use to detect very close and very small blobs in high resolution fluorescence images?

Context I am developing a pipeline to automate the detection of small, almost circular, bright blobs (4px) (see first image below) on high-resolution fluorescence images (2048px) and later to assign ...
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1 answer
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Will there be any changes in the model's performance due to the usage of very small batch sizes?

I am trying to run a code that has a batch size around 28. I can run the program on my GPU with this batch size. But, when I modify the code for my requirements and try to run, it is showing an run-...
1 vote
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60 views

Are goal-reaching and optimizing the utility function special cases of performance measure?

In AIMA, performance measure is defined as something evaluating the behavior of the agent in an environment. Rational agents are defined as agents acting so as to maximize the expected value of the ...
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Simple example for average log-probability

Consider the following statements from Chapter 5: Machine Learning Basics from the book titled Deep Learning (by Aaron Courville et al.) For tasks such as classification, classification with missing ...
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How to properly report results for a medical image segmentation task?

Let’s consider a 2-class / binary segmentation problem where c=0 for background (healthy tissues) and c=1 for foreground (...
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1 answer
102 views

How do I know what a good mean absolute error value is? [closed]

I have just run an MAE calculation for my machine learning models and the results show: SVM MAE = 28.850 deg. Random Forest MAE = 33.832 deg. How do I know what a good MAE value is? What is the ...
2 votes
1 answer
103 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 ...
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45 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 ...
2 votes
1 answer
82 views

How would the performance of federated learning compare to the performance of centralized machine learning when the data is i.i.d.?

How would the performance of federated learning (FL) compare to the performance of centralized machine learning (ML), when the data is independent and identically distributed (i.i.d.)? Moreover, what ...
4 votes
1 answer
323 views

How to evaluate an RL algorithm when used in a game?

I'm planning to create a web-based RL board game, and I wondered how I would evaluate the performance of the RL agent. How would I be able to say, "Version X performed better than version Y, as ...
1 vote
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64 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
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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
1 answer
114 views

How do I check that the combination of these models is good?

I've selected more than 10 discriminative (classification) models, each wrapped with a BaggingClassifier object, optimized with a ...
2 votes
1 answer
450 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 ...
0 votes
1 answer
196 views

How is the performance of a model affected by adding a ReLU to fully connected layers?

How significant is adding a ReLU to fully connected (FC) layers? Is it necessary, or how is the performance of a model affected by adding ReLU to FC layers?
13 votes
4 answers
3k 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?
2 votes
0 answers
37 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 ...
1 vote
0 answers
28 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 ...
0 votes
1 answer
412 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 ...
5 votes
2 answers
155 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 ...
1 vote
0 answers
38 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 ...
2 votes
4 answers
3k 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 ...
1 vote
1 answer
1k 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-...
2 votes
1 answer
151 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 ...
1 vote
1 answer
55 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). ...
2 votes
0 answers
36 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. ...
1 vote
0 answers
39 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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24 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
1 answer
60 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 ...
5 votes
1 answer
57 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 ...
1 vote
0 answers
25 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 ...
5 votes
2 answers
2k 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 ...
2 votes
0 answers
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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 ...
1 vote
0 answers
49 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
0 answers
60 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 ...
7 votes
1 answer
605 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 ...
6 votes
4 answers
515 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 "...