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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317 views

Are there ways to learn and practice Deep Learning without downloading and installing anything?

As per subject title, are there ways to try Deep Learning without downloading and installing anything? I'm just trying to have a feel of how this work, not really want to go through the download and ...
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1answer
77 views

Deep Q Learning for Simple Game Not Effective

This is a follow-up question about one I asked earlier. The first question is here. Basically, I have a game where a paddle moves left and right to catch as much "food" as possible. Some food is good (...
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40 views

Untrained CNNs as feature extractors?

I've heard somewhere that due to their nature of capturing spatial relations, even untrained CNNs can be used as feature extractors? Is this true? Does anyone have any sources regarding this I can ...
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2answers
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How to make deepfake video without a fancy PC?

Is there any way to make deepfake videos without a fancy computer? For example, run the DeepFaceLab on a website so your own computer won't get involved?
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0answers
25 views

How do deep fakes get the right encoding for both people?

Deep fakes work by using a single encoder but then having a different decoder for different people. But I wondered what if the encoder encodes say "closed eyes" of person A as the same code for "...
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1answer
57 views

What is the difference between asymmetric and depthwise separable convolution?

I have recently discovered asymmetric convolution layers in deep learning architectures, a concept which seems very similar to depthwise separable convolutions. Are they really the same concept with ...
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1answer
33 views

How do I tag the most interesting parts of a video?

This is a follow-up question from my previous question here. I'm new to ML/DL, and one thing I need to do is to use a machine or deep learning video attention model which as the name suggests, can tag ...
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1answer
55 views

How to detect patterns in a data set of given IP addresses using a neural network?

How to detect patterns in a data set of given IP addresses using a neural network? The data set is actually a list of all the vulnerable devices on a network. I want to use a neural network that ...
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2answers
30 views

What kind of output should be used for predicting angles in DNNs?

I am building a model which predicts angles as output. What are the different kinds of outputs that can be used to predict angles? For example, output the angle in radians cyclic nature of the ...
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17 views

Where to find pre-trained models for multi-camera people tracking?

I need to build a multi-camera people tracking system and I have no idea how to start. I read ML for Dummies and I've watched a lot of youtube classes/conferences and read a lot of articles about ML/...
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1answer
28 views

What does the words “coarse” & “fine” means in the context of computer vision and semantic segmentation?

I was reading the well know paper Fully Convolutional Networks for Semantic Segmentation and throughout the whole paper they talk use the term fine and coarse. I was wondering what the meant. The ...
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0answers
39 views

Grouped Text classification

I have thousands groups of paragraphs and I need to classify these paragraphs. The problem is that I need to classify each paragraph based on other paragraphs in the group! For example, a paragraph ...
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1answer
103 views

Inception Resnet V2 Feature Extraction Layer?

I am working with Inception Resnet V2 with "Imagenet" pre-trained model for face recognition. So that I tend to ignore the Fully Connected Layer to get the extract feature. But I'm so confused of what ...
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2answers
58 views

What are the major differences between cost, loss, error, fitness, utility, objective, criterion functions?

I find the terms cost, loss, error, fitness, utility, objective, criterion functions to be interchangeable, but any kind of minor difference explained is appreciated.
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AI for Warcraft 3 Dota

I want to create AI for Dota1. Is it possible to create AI for Warcraft 3? How Open AI works in Dota2? I want to know more about algorithms what are in foundation in Open AI.
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25 views

Pooling vs Subsampling: Multiple Definitions?

I have seen people using pooling and subsampling synonymously. I have also seen people use them as different processes. I am not sure though if I have correctly inferred what they mean, when they use ...
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1answer
96 views

Which approach can I use to generate text based on multiple inputs?

I have a little experience in building various models, but I've never created anything like this, so just wondering if I can be pointed in the right direction. I want to create (in python) a model ...
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29 views

Al online course

I have learned embedded systems Now I need an online course in artificial intelligence to make a project mixing between them (es&ai)
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44 views

How to use the LSTM layer in PPO architecture?

What is the best way of using the LSTM layer in PPO architecture? Should I use them in the first layer of both actor and critic, or use them just before the final layer of these networks? Should I ...
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1answer
36 views

Understanding arrangement of applying filters to input channels

I was watching a video about Convolutional Neural Networks: https://www.youtube.com/watch?v=SQ67NBCLV98. What I'm confused about is the arrangement of applying the filters' channels to the input image ...
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25 views

Is convergence to a local minima more likely with transfer learning?

While doing transfer learning where my two problems are face-generation and car-generation is it likely that, if I use the weights of one problem as the initialization of the weights for the other ...
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1answer
34 views

Use deep learning to rank video scenes

I'm new to machine learning and especially, deep learning. Given a video (and it's subtitle), I need to generate a 10-second summary out of this video. How can I use ML and DL to produce the most ...
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1answer
43 views

How can I use one neural network for both players in Alpha Zero (Connect 4)?

First of all, it is great to have found this community! I am currently implementing my own Alpha Zero clone on Connect4. However, I have a mental barrier I cannot overcome. How can I use one neural ...
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34 views

Could the Jensen-Shannon divergence and Kullback-Leibler divergence be used as loss functions of non-generation problems?

If I understand correctly, the KL divergence is a measure of information loss between a ground truth distribution $P$ and a predicted distribution $Q$, and the Jensen-Shannon divergence is the mean of ...
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1answer
36 views

What is the purpose of the noise injection in the generator network of a GAN?

I do not understand why with enough training how the generator cannot learn all images from the training set as a mapping from the latent space - It is the absolute optimal case in training as it ...
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23 views

How do I do further (domain specific) pre-training with Google BERT in preparation for subsequent fine-tuning?

Another way to say this is "can you create a .ckpt file created from the final output of BERT?" Here's a recent paper that talks about additional fine-tuning.
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1answer
30 views

Query regarding the minmax loss function formulation of the training of a Generative Adversarial Network (GAN)

Just needed a clarification on the training procedure for a standard GAN. Of my understanding the loss function to optimize is a min max (max min causing mode collapse due to focus on one class ...
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0answers
17 views

Language Model from missing data

I want to learn how a set of operations (my vocabulary) are composed in a dataset of algorithms (corpus). The algorithms are a sequence of higher level operations which have varying low-level ...
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1answer
57 views

Why can't neural networks learn functions outside of the specified domains?

I understand that neural nets are fundamentally interpolative tools. Meaning, given a training dataset, a well trained neural net can approximate values within the domain of the training dataset. ...
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7 views

Current state of MoE models

I've been reading about Mixture of Expert models, and I've noticed that there is very little new work being produced in this subfield. Has there been a better method discovered? Why aren't more people ...
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1answer
17 views

Is a trained model in keras is saved with the weights for max accuracy?

Does a model trained in keras (tensorflow backend) saves the weights with max accuracy and minimum losses or does it simply saves the weights from the last epoch? If it is the latter then how do I ...
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33 views

Reward problem in A2C with multiple simultaneous discrete actions

I've built an A2C model whose actor's network has two different kinds of discrete actions, so the critic would take state and action (note that critic takes 2 actions because in each timestep we will ...
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1answer
91 views

How to Mask an image using Numpy/OpenCV?

I am detecting wheels with a deep learning algorithm. The algorithm gives me the coordinates of those rectangles. I want to keep data that is in the rectangles of the image. I created rectangles as a ...
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9 views

When should I stop the object detection model training while mAP are not stable?

I am re-training the SSD MobileNet with 900 images from the Berkeley Deep Drive dataset, and eval towards 100 images from that dataset. The problem is that after ...
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1answer
51 views

How can I learn tensors for deep learning?

I've seen in most deep learning papers use tensors. I understood what tensors are, but I want to dive into them, because I think that might be beneficial for further studies in Artificial Intelligence....
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2answers
41 views

Are feature maps merged or are they passed on as they are?

I am unsure about the following parts of the architecture and mechanics of convolution layers in CNNs. Possibly, this is implementation-dependent though. First question: Say I have 2 convolution ...
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1answer
50 views

If the goal of training of a GAN is to have $P_g=P_{data}$, shouldn't this produce the exact same images?

Referring to the blog, Image Completion with Deep Learning in TensorFlow, it clearly says that we would want a generator $g$ whose modeled distribution fits our dataset $data$, in other words, $P_{...
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2answers
71 views

What is the difference between a machine learning engineer and deep learning engineer?

What is the difference between a Machine Learning Engineer and Deep Learning Engineer and an AI developer? What would be their daily tasks at the office?
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0answers
19 views

Improving the performance of a DNN model

I have been executing an open-source Text-to-speech system Ossian. It uses feed forward DNNs for it's acoustic modeling. The error graph I've got after running the acoustic model looks like this: ...
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0answers
58 views

How can I incrementally train a Yolo model without catastrophic forgetting?

I have successfully trained a Yolo model to recognize k classes. Now I want to train by adding k+1 class to the pre-trained weights (k classes) without forgetting previous k classes. Ideally, I want ...
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1answer
39 views

Why does keras model get bigger after training?

I notice that I create a model using tensorflow.keras.Sequential(), save it and the file size is around 5 MiB, but after I call ...
2
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1answer
28 views

Spikes in of Train and Test error

I learn a DNN for image recognition. During each epoch, I calculate mean loss in the training set. After each epoch, I calculate loss and number of errors over both training and test set. The problem ...
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1answer
78 views

Suicide Predictor and Locator

Suicide is on the increase in my country and most victims tend to leave early traces from text messages, social media accounts, search engine queries. So I came up with the idea to develop an AI ...
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0answers
61 views

How do I determine the generalisation ability of a neural network?

I am trying to ascertain if my neural network is able to generalize or if it’s simply using memory/overfitting to solve a task. I would like my model to generalise. Currently, I train the neural ...
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1answer
47 views

What is the most common practice to apply batch normalization?

For a deep NN, should I generally apply batch normalization after each convolution layer? Or only after some of them? Which? Every 2nd, every 3rd, lowest, highest, etc.?
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1answer
51 views

How should I detect an object in a camera image?

I would like to create a model, that will tell me if one type of object is in an image or not. So, for example, I have a camera and I would like to see when one object gets into the shot. Object ...
4
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1answer
150 views

Can a vanilla neural network theoretically achieve the same performance as CNN?

I perfectly understand that CNN takes into account the local dependency of each pixel to the nearby pixels. In addition, CNNs are spatially invariant which means that they are able to detect the same ...
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0answers
36 views

Paper & code for “unsupervised domain adaptation” for regression task

Does anyone know a paper or code that does "unsupervised domain adaptation" for regression task? I saw most of the papers were benchmarked on classification tasks, not regression. I want to do ...
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0answers
34 views

Is it possible to use adversarial training to learn invariant features?

Given a set of time series data that are generated from different sites where all sites are investigating the same objective but with slightly different protocols. Is it possible to use adversarial ...
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0answers
20 views

How to implement Multiple Neural network architecture, connected in parallel and series in Keras or Pytorch

Hello Dear StackExchange members, I want to make a deep network as shown in the image. I want each 'network 1 to look at the specific part of the input and I don't want to divide my input beforehand ...