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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113 views

Why is this ResNet50 misclassifying objects?

I'm new to Deep Learning, and I have some conceptual problems. I followed a simple tutorial here, and trained a model in Keras to do image classification on 10 classes of logos. I prepared 10 classes ...
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26 views

Why are researchers focused on deep learning based stereo depth/disparity methods instead of non deep learning ones?

In recent years if you are working on stereo depth/disparity algorithms, it seems like you will only ever get your paper accepted to CVPR/ICCV/ECCV if there's some deep learning involved in it. A lot ...
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37 views

What is the underlying model of IBM Watson Assistant and Microsoft LUIS?

As I stated in my question, I would like to know the underlying pipeline and machine learning models that are used to classify intents and identify entities in IBM Watson Assistant and Microsoft LUIS ...
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63 views

Is there a deep learning-based architecture for digit localisation?

I'm new to object detectors and segmentation. I want to localize digits on a plate as fast as possible. All images of the dataset are normalized to $300 \times 60$. There are different approaches to ...
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Assigning Weighting Factors

I have a hypothetical example that closes to my research problem: Assume you are a boss and you have different types of tasks that you need to assign to your employee. Sensitive task (very classified)...
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How to choose the suitable Neural Network Architecture for Regression Tasks

so I'm working on a Project where I want to predict the Vehicle Position from the Vehicle Data like speed, acceleration etc.. now the data that I have comes also with a timestamp for each sample ( I ...
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1answer
126 views

What should load_mask() return if an image doesn't have any objects? (Mask RCNN)

I want to use Mask RCNN to do image segmentation. I need to override the load_mask function for the dataset class. I know this function should return mask tensors and class ids of objects in an image. ...
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52 views

Torch CNN not training

I am completely new to CNN's, and I do not quite know how to design or use them efficiently. That being said, I am attempting to build a CNN that learns to play Pac-man with reinforcement learning. I ...
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25 views

How can I detect fast and slow motion in videos?

I'm trying to detect if a given video shot is fast or slow motion. Basically, I need to calculate a "video motion" score in a given video sequence, meaning how fast or slow motion the video is. For ...
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How do I recover the 3D structure of a layer after a fully-connected layer?

I want to implement a CNN, but I want to explore what happens when my first layer is a fully-connected one. I still want to use convolutions, of course, but I want to apply them after the first layer. ...
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108 views

Spike detection in time series using Artificial Neural Networks

I'm quite new in ANNs. I intend to use ANNs for predicting spike points in time series right before they happen. I've already used LSTM for another scenario, and I know that they can be used in ...
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43 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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46 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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2answers
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What is the exact output of the Inception ResNet V2's feature extraction layer?

I am working with the Inception ResNet V2 model, pre-trained with ImageNet, for face recognition. However, I'm so confused about what the exact output of the feature extraction layer (i.e. the layer ...
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What are the main technologies needed to build an AI for Warcraft 3's mod DotA?

What are the main technologies needed to build an AI for Warcraft 3's mod Defense of the Ancients (DotA)? Maybe I can take inspiration from OpenAI's work.
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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
72 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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122 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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26 views

How to voxelize multiple frames at the time and append them together?

I'm trying to implement this approach for object detection and tracking. In this approach, the first step is voxelize each frame to construct a 3D tensor, the second step is to append multiple voxels ...
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1answer
75 views

Validation Loss Fluctuates then Decrease alongside Validation Accuracy Increases

I was working on CNN. I modified the training procedure on runtime. As we can see from the validation loss and validation accuracy, the yellow curve does not fluctuate much. The green curve and red ...
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64 views

How to define cost function for custom nonlinear functions?

For logistic regression, the Cost function is defined as: \begin{equation} Cost(h_{\theta}(x)-y) = -ylog(h_{\theta}(x))-(1-y)log(1-h_{\theta}(x)) \end{equation} I now have a nonlinear function \begin{...
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How to debug and find neurons that most influenced a pixel in the output image?

I'm building CNN network of Image to Image. After training, I have some bad results in part of the Image. I would like to find the neurons that most influenced those pixels and do retraining only ...
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57 views

Transposed convolution as upsampling in DCGAN

I read several papers and articles where it is suggested that transposed convolution with 2 strides is better than upsampling then convolution. However implementing such model with the transposed ...
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16 views

Cluster 2 AWS instances for deep learning model training

I am working on a CNN model for image classification. Currently a single AWS instance (p2xlarge) used for model training. I would like to have one more AWS instance and distribute the load across ...
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114 views

How to build a neural network that can learn to predict output images?

I am working with a dataset where each input sample is a matrix, and the output corresponding to each input is also a matrix (of shape (400, 10)). The input samples ...
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33 views

What methods are there to generate artificial training examples based on existing training examples?

I have a small dataset (117 training examples) and many features (4005). Each of the training examples is binary labeled (healthy / diseased). Each feature represents the connectivity between two ...
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28 views

Hinton's reading list from the removed Coursera MOOC

Geoffrey Hinton's Coursera MOOC was recently discontinued: https://twitter.com/geoffreyhinton/status/1085325734044991489?lang=en The videos however are still available at both on Youtube and on ...
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Is making lot of 1 versus other model efficient?

I've got classification problem on image, I have 10 classes and when I fine tuned my model on it (I tried VGG, Xception, resnet etc) I have approximatly 83% validation accuracy. I was wondering if ...
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204 views

Why does the BERT encoder have an intermediate layer between the attention and neural network layers with a bigger output?

I am reading the BERT paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. As I look at the attention mechanism, I don't understand why in the BERT encoder we have ...
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161 views

How do the sine and cosine functions encode position in the transformer?

After going through both the "Illustrated Transformer" and "Annotated Transformer" blog posts, I still don't understand how the sinusoidal encodings are representing the position of elements in the ...
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407 views

Deep Learning on how to find out the body measurement (e.g. shoulder length, waist, hips, legs length etc) from mobile camera captured images?

I do understand that there are plenty of mobile apps available for body measurement (e.g. MTailor) or creating 3D model (3dlook). What I would like to find out is how we can use deep learning to ...
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54 views

Training, test, dev split in speech recognition

Unfortunately there is no speech-recognition or speech-to-text tag yet so I go with the ...
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3answers
135 views

Extracting algebraic constraints from the input data

I would appreciate your help with this (naive) question of mine. Given the set of points located on a circle, $x_{i}, y_{i}$ as the input data, Can a deep/machine learning algorithm infer that radius ...
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265 views

How does DARTS compare to ENAS?

How does DARTS compare to ENAS? Which one is better or what advantages does they each have? Links: DARTS: Differentiable Architecture Search Efficient Neural Architecture Search via Parameter ...
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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 ...
2
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1answer
315 views

Should I apply ReLU to non negative output?

Suppose I want to predict the position of a sensor based on its reading. I can first predict the unit vector and predict the distance to be multiplied to this vector. And I know that distance will ...
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1answer
94 views

What are some limitations of using Collaborative Deep learning for Recommender systems?

Recently I worked on a paper by Hao Wang, Collaborative Deep learning for Recommender Systems; which uses a two way tightly coupled method, Collaborative filtering for Item correlation and Stacked ...
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49 views

Setting learning rate as negative number for wrong train cases

I was watching a video which tells a bit about reinforcement learning, and I learnt that If the robot makes wrong movement then they train the network with negative learning rate. From this method, ...
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67 views

Regarding L0 sparsification of DNNs proposed by Louizos, Kingma and Welling

I am reading the paper on $\ell_0$ regularization of DNNs by Louizos, Welling and Kingma (2017) (Link to arxiv). In Section 2.1 the authors define the cost function as follows: $$ \mathcal{R}\left( \...
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33 views

Use of backpropagation for weight updates in a combination of 2 neural networks

Every neural network updates its weights through back-propagation. How is back-propagation used for updating weights in a combination of 2 or more neural networks (e.g.:CNN-LSTM, GAN-CNN, etc.). For ...
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23 views

Is there any deep learning object detection algorithms that can work without bounding boxes annotated data?

For example Haar Cascade can be trained using only positive and negative examples, you don't need any bounding box annotations. But it not a deep learning approach. Another example can be the most ...
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1answer
95 views

Price difference predictions curve almost vanished

With a team, we are studying how it is possible to predict the price movement with high-frequency. Instead of predicting the price directly, we have decided to try predicting price difference as well ...
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0answers
25 views

Data / model preparation for spatio-temporal deep-learning analysis for traffic congestion events detection

I am preparing the Bus movement dataset for deep learning (ANN/CNN/RNN) analysis for congestion events detection. This is an extension to my original question, which can be located at 'Deep learning ...
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39 views

How exactly is equivariance achieved in capsule neural networks?

I have read quite a lot about capsule networks, but I cannot understand how the squashed vector would also rotate in response to rotation or translation of the image. A simple example would be helpful....
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0answers
31 views

In CNN (Convolutional Neural Network), does the combination of previous layer's filters make next layer's filters?

I know that the first layer uses a low-level filter to see the edge information. As the layer gets deeper, it will represent high-level (abstract) information. Is it because the combinations of ...
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0answers
81 views

How do GAN's generator actually work?

I have implemented DCGAN's myself and have been studying GAN's for over a month now. Now I am implementing the pggans but I encountered a sentence When we measure the distance between the ...
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62 views

deep learning, memorizing the input data not learning

I have 1000 data sentences in Turkish like "a esittir b arti c". The example sentence means "a = b + c". I basically want to translate mathematical Turkish sentences into math equations. For example, ...
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168 views

Deep learning model (LSTM) with temporal and non temporal attributes

I'm working on a project to predict the usage of all the files in a filesystem in near future based on the metadata of the file system for past 6 months. I've got the following attributes about the ...
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180 views

Normalizing height data for CNN

A task I’m working on at the moment requires a CNN with a height map as one of the inputs. This is a matrix of floating point values in which each point is the height of that point above sea level. I’...
2
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1answer
264 views

Unable to overfit using MLP

I'm building a 5-class classifier with a private dataset. Each data sample has 67 features and there are about 40000 samples. Samples of a particular class were duplicated to overcome class imbalance ...

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