Questions tagged [deep-learning]

For questions related to deep learning, which refers to a subset of machine learning methods based on artificial neural networks (ANNs) with multiple hidden layers. The adjective deep thus refers to the number of layers of the ANNs. The expression deep learning was apparently introduced (although not in the context of machine learning or ANNs) in 1986 by Rina Dechter in the paper "Learning while searching in constraint-satisfaction-problems".

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27 views

Is there any relationship between the batch size and the number of epochs?

I am currently running a program with a batch size of 17 instead of batch size 32. The benchmark results are obtained at a batch size of 32 with the number of epochs 700. Now I am running with batch ...
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How general is generalization?

I am sorry but I have to explain my question using an example, I do not know how to ask it in proper scientific terms. Let's assume, I have trained a deep learning model on classifying hand gestures, ...
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Brain tumour detection using CNN

I have a fairly basic mathematical and implementational understanding of ML algorithms and CNNs, and I am trying to think of an approach for this task: https://www.kaggle.com/c/rsna-miccai-brain-tumor-...
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How to assess the goodness of a text generation algorithm

Take a RNN network fed with Shakespeare and generating Shakespeare-like text. Once a model seems mathematically fine, as can be assessed by observing its loss and accuracy over training epochs, how ...
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Input and output data of digital twin (UAV)

Hi im a senior working with a team on a digital twin of a UAV. I will preface by saying that i just started learning about deep learning. My task is to build a neural network (2 layer feed forward ...
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How to find space utilised and free area in a room from Images [closed]

I have Multiple images of the room. How could I calculate space utilised and free space in the room from those images.
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Counting number of coaches in a train from real time video feed

I have a real time video feed of a train platform. I was able to detect coaches using CNN based model. But how can I calculate number of coaches in the train that passed the platform as well as the ...
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Data Augmentation for Object Detection - Polygon Region Shape

I'm looking to run a Mask RCNN code on my dataset of about 2700 images. The images are too large and I would like to resize them, and I would also like to add some shear, scale and zoom augmentations. ...
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What all are the known reasons for the decline in the performance of a neural network if we keep on increasing the depth of it?

Progress in many application tasks in artificial intelligence is achieved by increasing the depth of the neural networks. But if we keep on increasing the number of layers in the neural network, the ...
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1answer
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Is "width of a neural network" a wrong phrase?

Depth of the neural network is equal to the total number of layers in the neural network except input layer. so, neural network with more number of layers are called deep neural networks. Width, in ...
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How does the margin classifier lower the generalization error?

I am a beginner in machine learning, and I started learning about machine learning starting from linear classification. I am currently reading about margin classifier, and even though i understand the ...
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multi agent deep deterministic policy gradient for discrete actions

I am solving a multi agent problem where each agent has a critic and actor. The problem I am solving has discrete actions and discrete states. I came cross multi-agent deep deterministic policy ...
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Should I repeat lengthy deep learning experiments to average results ? How to decide how many times to repeat?

I am doing my MSc thesis on deep learning. My model takes many hours to train. Part of what I do is trying different parameters and settings hoping that they will achieve different results. But I ...
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Where can I read about upsampling methods in detail?

In deep learning, we encounter the upsample blocks several times, especially when we deal with images. Consider the following statements from description regarding UPSAMPLE in PyTorch The algorithms ...
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Building Gaussian Mixture VAE using pytorch

I am trying to implement GMM-VAE model using torch. Basically I have a problem implementing for instance equation (1c) where the posterior distribution $p(x|z,w)$ is a Gaussian with the mean and ...
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Continue teaching pre-trained network without forgetting previous data set

I have a rather interesting problem here; I work in the field of image classification for quality assurance. For this I have a dataset of about 1 million images, which I have used to train different ...
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22 views

Machine learning with raw data alone / or raw data with its statistics

My question is very general and it does not originate from a specific problem. Let's assume that, through experience, we have learned that some statistical property of a set of data is important in ...
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1answer
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What is better to use: early stopping, model checkpoint or both?

I want to get a model which works best, what should I go for while training the model, ModelCheckpoint, EarlyStopping, or both?
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How to increase accuracy of image orientation classification (Left, Right, Center)?

I am working on classifying images in "Left", "Right", "Center", "Back". Training and Validation images look like this: The images are "Left", "...
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1answer
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Why should one ever use ReLU instead of PReLU?

To me, it seems that PReLU is strictly better than ReLU. It does not have the dying ReLU problem, it allows negative values and it has trainable parameters (which are computationally negligible to ...
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Is it normal that the values of the LogSoftmax function are very large negative numbers? [closed]

I have trained a classification network with PyTorch lightning where my training step looks like below: ...
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1answer
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Multi Agent Deep Reinforcement Learning for continuous and discrete action

I am looking to have a cooperative multi agent reinforcement learning framework where one agent has a discrete action space and another agent has a continuous action space. Is there a way to do this ...
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1answer
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How to approach a blackjack-like card game with the possibility of cards being counted?

Consider a single-player card game which shares many characteristics to "unprofessional" (not being played in casino, refer point 2) Blackjack, i.e.: You're playing against a dealer with ...
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What's wrong with my understanding of how RNNs work?

Recently, I've been trying to derive the mathematics behind various Neural Network structures. I managed to derive the MLP and tested it to be on par with a Keras implementation (Using the MNIST ...
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1answer
58 views

Different ways to calculate backpropagation derivatives, any difference?

I'm studying error backpropagation in neural networks. I am interested in why we use only one path on the computational graph to get the value of the derivative for a weight? I ask the question ...
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What are strategies for data driven weights initialization?

I am beginner in deep learning and currently training a few neural networks (Pytorch) for problems in audio and speech. For my tasks, simple feed-forward networks are working well enough. I use basic ...
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What are mathematically the factors of variation in deep learning?

The following paragraph from an answer tells us about factors of variation Factors of variation are some factors which determine varieties in observed data. If that factors change, the behaviour of ...
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2answers
132 views

What is the optimal score for Tic Tac Toe for a reinforcement learning agent against a random opponent?

I guess this problem is encountered by everyone trying to solve Tic Tac Toe with various flavors of reinforcement learning. The answer is not "always win" because the random opponent may ...
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1answer
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Tensorflow object detection model total loss starts out good, but suddenly explodes up to high loss numbers

I'm training a Tensorflow object detection model with approx. 7500 images of two classes, which contains approx. 10,000 classes per class. I'm using Tensorflow 2.6.0, in case that is relavent. I am ...
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Can I extend Graph Convolutional Networks to graphs with weighted edges?

I'm researching spatio-temporal forecasting utilising GCN as a side project, and I am wondering if I can extend it by using a graph with weighted edges instead of a simple adjacency matrix with 1's ...
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Preprocessing images for test and validation datasets for training a convolutional neural network (CLAHE)?

I'm training a convolutional neural network for image classification,and i want to preprocess the images, for example with the CLAHE method. I'm not sure if this preprocessing has to be used on the ...
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Mapping ground truth to downsampled embeddings

I am currently pulling embeddings out of the mid layers of PSPNet. I was wondering if anyone knows of a way to see what pixels in the ground truth map to the pixels in the intermediate layers? e.g. we ...
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Expression Transfer Deep Learning Problem

I have old video and I want to keep the person's face in the video but I want to transfer my facial expressions to that video. Is there any better alternative to first order motion model for that task ...
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1answer
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My weights for binary classification are not getting updated [closed]

I am very new to this pytorch and neural networks.I am stuck in training one model since last 1 week. My model paramters are not getting updated after each epoch. Also,...
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is there any proof that metric learning cannot achieve better on image classification task than accepted models (resnet etc)?

Everything is in the title. Metric learning seems to be closer to our way of thinking than the best performing models (supervised learning CNNs-based models like resnet or efficientnet). I was looking ...
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Which model is more efficient and why?

Suppose, I have two NN models: CNN model Sequential NN model They are solving the same problem. The data points have the same number of features. In the case of #1, we used 0.6 million data points, ...
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1answer
30 views

How to make an output independent of input feature in neural networks?

Is there a way to make a certain output dimension of a neural network independent of a particular feature dimension? For example, I have a function $f_{\theta} : \mathcal{R}^{10} \rightarrow \mathcal{...
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1answer
41 views

How do CNNs handle inputs of different sizes and shapes?

I am new to deep learning so feel free to correct me where I am wrong. Imagine this scenario where we have a 7 * 7 input. We want to slide a 3 * 3 filter with a stride of 3 and padding of zero over ...
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How does back propagation adjust the hidden layers' weights and biases?

I'm new to neural networks and trying to figure out its fundamentals but I cannot fully understand the back propagation algorithm. In back propagation, I understand we want to go backwards from the ...
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25 views

Rebirth Architecture for Deep Learning

Intro: Lots of Machine Learning methods are inspired Biology, Nature, Physics, Neurology... I just thought of a Deep Learning approach inspired on religion: Rebirth Network Some eastern religions ...
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1answer
64 views

Transformer model is very slow and doesn't predict well

I created my first transformer model, after having worked so far with LSTMs. I created it for multivariate time series predictions - I have 10 different meteorological features (temperature, humidity, ...
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11 views

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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Why is the prior on the latent variable standard gaussian in VAE?

While training a standard VAE, we assume that the prior on the latent variable Z is the standard gaussian and we use KL divergence to push the posterior as close as possible to the standard gaussian. ...
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1answer
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Do solving system of linear equations required anywhere in contemporarty deep learning?

Consider the following from Numerical Computation chapter of Deep Learning book Machine learning algorithms usually require a high amount of numerical computation. This typically refers to algorithms ...
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How to calculate computational efficiency of Deep Learning Models?

I am trying to make a comparison between two simple 5 layer neural network models. One of the models has 3 frozen layers as I've implemented transfer learning in that architecture. The other is ...
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How much depth is recommended to study constrained optimization for deep learning?

I am studying the chapter named Numerical Computation from the deep learning textbook In the chapter, there is a section named Constrained Optimization. The authors recommended to read the portion of ...
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1answer
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What does it mean "having Lipschitz continuous derivatives"?

We can enforce some constraints on functions used in deep learning in order to guarantee optimizations. You can find it in Numerical Computation of the deep learning book. In the context of deep ...
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1answer
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Which class of functions are quite complicated in deep learning?

Deep learning is a field in which we need neural networks that are deep enough to carry on our task. The important fucntions in deep neural networks can be classified in to three classes: activation ...

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