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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9 views

What are some real-world products/applications that can be developed using GANs?

GANs have shown good progress across a wide variety of domains ranging from image translation, image generation, text to image synthesis, audio/video generation, image super-resolution and many more. ...
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What is symbol-to-number differentiation?

I recently came across symbol-to-symbol and symbol-to-number differentiation, out of which symbol to symbol seemed fairly straightforward - the computational graph is extended to include gradient ...
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CNN High Variance across multiple trained models, what does it mean?

Background: I have a 2D CNN model that I am applying to a regression task with some uniquely extracted spectrograms. The specifics of the data set are mostly irrelevant and very domain specific so I ...
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Inner working of Bidirectional RNNs

I'm trying to understand how Bidirectional RNNs work. Specifically, I want to know whether a single cell is used with different states, or two different cells are used, each having independent ...
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1answer
20 views

Running 10 epochs on the Food-101 dataset

I’m currently working on the Food-101 dataset. I want to train a model that is greater than 85% accuracy for top-1 for the test set, using a ResNet50 or smaller network with a reasonable set of ...
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35 views

Where can I get exercises and problems to implement machine learning models and algorithms?

I'm an intermediate machine learning student and want to get more detailed and specific practical intuition about artificial intelligence. I have made a couple of searches over the well-observed ...
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1answer
34 views

Is the derivative of the loss wrt a single scalar parameter proportional to the loss?

I am wondering about the correlation between the loss and the derivative of the loss wrt a single scalar parameter, with the same sample. That means: considering a machine learning model with ...
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How to implement taylor expansion of progressive Convolutional NN?

I intend to implement CNN like progressively expanded neural network in Keras. The basic idea is the first input node can be decomposed into multiple nodes with different orders and coefficient, then ...
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1answer
37 views

Alternatives to U-Net for biomedical image segmentation

Soon I will be working on biomedical image segmentation (microscopy images). There will be a small amount of data (a few dozens at best). My question is really straightforward: is there a neural ...
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tf.data.Dataset: The `batch_size` argument must not be specified for the given input type [migrated]

I'm using Talos and Google colab TPU to run hyperparameter tuning of a Keras model. Note that I'm using Tensorflow 1.15.0 and Keras 2.2.4-tf. ...
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How can I perform object detection by cutting the image into many pieces each containing one object?

Our task is to do a special object detection: In the traditional case, the neural network will output some rectangle bounding boxes. But in our case, the network should output many nearly-vertical ...
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What does it mean to have epochs=30 in Keras' fit method given certain data?

I have read a lot of information about several notions, like batch_size, epochs, iterations, but because of explanation was without numerical examples and I am not native speaker, I have some kind of ...
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What is relation between gradient descent and regularization in deep learning?

Gradient descent is used to reduce the loss and regularization is used to fight over-fitting. Is there any relation between gradient descent and regularization, or both are independent of each other?...
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What are examples of commonly used feature and readout maps?

It is well-known that deep feedforward networks can approximate any continuous function from $\mathbb{R}^k$ to $\mathbb{R}^l$, (uniformly on compacts). However, in practice feature maps are ...
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Are there any commonly used discontinuous activation functions?

Are there any commonly used activation functions (e.g. that take values in $(0,.5)\cup (.5,1)$)? Preferably for classification? Why? I was looking for commonly used activation functions on Google, ...
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2answers
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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 ...
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1answer
21 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). ...
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Why do DeconvNet use ReLU in the backward pass?

Why does DeconvNet (Zeiler, 2014) use ReLU in the backward pass (after unpooling)? Are not the feature maps values already positive due to the ReLU in the forward pass? So, why do the authors apply ...
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Do RNN solves the need for LSTM and/or multiple states in Deep Q-Learning?

Introduction I am trying to setup a Deep Q-Learning agent. I have looked that the papers Playing Atari with Deep Reinforcement Learning as well as Deep Recurrent Q-Learning for Partially Observable ...
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How do we minimize loss for a single neuron with a feedback?

Suppose we had a series of single-dimensional data points $X = \{x_1, x_2, \dots, x_n \}$, where $n$ is the number of data points and there corresponding output values $T = \{t_1, t_2, \dots, t_n \}$. ...
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Is there any wrong in my focal loss derivation?

Assume $\mathbf{X} \in R^{N, C}$ is the input of the softmax $\mathbf{P} \in R^{N, C}$, where $N$ is number of examples and $C$ is number of classes: $$\mathbf{p}_i = \left[ \frac{e^{x_{ik}}}{\sum_{j=...
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length independent sequence classification methods

I am looking to do sequence classification using deep learning. The length of my sequences can vary from a few hundred to several tens of thousands of characters. I was wondering what is a good ...
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Is there any programming language designed by deep learning?

i mean Programming Language Implementation by Deep Learning / AI. I want to know this kind of information, any links and news are very welcome. sure AI drawing picture and design PCB, so why not.
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Is ReLU a non-linear activation function?

According to this blog post The purpose of an activation function is to add some kind of non-linear property to the function The sigmoid is typically used as an activation function of a unit of a ...
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1answer
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How can I develop a reinforcement learning agent that plays memory cards game?

I am new to RL, and I am thinking of doing a little project. The goal of the project is to learn an agent play the memory game with cards. I already created the program for detecting the cards on the ...
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What are the pros and cons of deep learning and machine learning to develop a trading system?

As I want to start coding a new Trading AI in this year (first based on Python and later maybe in C++) I stumbled over the following question: Today, I would like to make a pro/contra list with you ...
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How to output a filter of equal size to the original image in Fully Convolutional Neural networks

I'm trying to perform a segmentation task on images of multiple sizes using fully convolutional neural networks. Currently, I'm using efficientnet as a feature extractor, and adding a deconvolution/...
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1answer
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What is the difference between batch and mini-batch gradient decent?

I am learning deep learning from Andrew Ng's tutorial Mini-batch Gradient Descent. Can anyone explain the similarities and dissimilarities between batch GD and mini-batch GD?
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1answer
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What effect does batch norm have on the gradient?

Batch norm is a technique where they essentially standardize the activations at each layer, before passing it on to the next layer. Naturally, this will affect the gradient through the network. I have ...
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How to use a cv2 image augmentation function with tensorflow tf.data.Dataset? [migrated]

I am using tf.data.Dataset to create my dataset and training a CNN with keras. I need to apply masks on the images, and the mask depends on the shape of the image, ...
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Why is my DQN model not getting better?

I've created a deep Q network. My model does not get better, and can't see what I'm doing wrong. I'm new to RL. Replay Memory ...
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1answer
56 views

Why does variational auto-encoder use the reconstruction loss?

VAE is trained to reduce the following two losses. KL divergence between inferred latent distribution and Gaussian. the reconstruction loss I understand that the first one regularizes VAE to get ...
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What could cause a big fluctuation of the loss in the last epochs of training an AlexNet?

I am training an AlexNet neural network, with about 12000 images which 80% is for training, 10% is for validation and another 10% is for testing. I have a problem in my plots. There is a big ...
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What is the difference between “out-of-distribution (generalisation)” and “(meta)-transfer learning”?

I'm trying to develop a better understanding of the concept of "out-of-distribution" (generalization) in the context of Bengio's "Moving from System 1 DL to System 2 DL" and the concept of "(meta)-...
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1answer
542 views

How should the neural network deal with unexpected inputs?

I recently wrote an application using a deep learning model designed to classify inputs. There are plenty of examples of this using images of irises, cats, and other objects. If I trained a data ...
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1answer
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How can I learn a graph given nodes with features in a supervised fashion?

I have a dataset and want to be able to construct a graph from it in a supervised fashion. Let's assume I have a dataset with N nodes, each node has e.g. 10 features. Out of these N nodes, I want to ...
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22 views

Human Aggression Detection Community, Competition and dataset

I'm looking for a community or competition website related to human aggression detection using Deep Learning in a video. Also, I'm looking for a dataset of human aggression activities. Any ...
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How can I get to a final output of shape $224 \times 224$, without FC layers, from a tensor of specific shape, in OpenPose?

I am approaching the implementation of the OpenPose algorithm for realtime human body pose estimation. According to the official paper OpenPose: Realtime Multi-Person 2D Pose Estimation using Part ...
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1answer
42 views

How to perform insect classification given two images of the same insect?

I'm relatively new to image classification. Currently, I am trying to classify insect images, using a convolutional neural network (CNN). When I ask a human expert to identify an insect, I usually ...
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1answer
21 views

Using AI to enhance customer service

I'm trying to find out how AI can help with efficient customer service, in fact call routing to the right agent. My usecase is given context of a query from a customer and agents' expertise, how can ...
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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. ...
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40 views

How to correctly implement self-play with DQN?

I have an environment where an agent faces an equal opponent, and while I've achieved OK performance implementing DQN and treating the opponent as a part of the environment, I think performance would ...
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1answer
72 views

How can I detect the frame from video streaming that contains a graffiti on city wall?

I am working on a graffiti detection project. I need to analyze data stream from a camera mounted sideways on a vehicle to identify graffiti on city walls and notify authorities with the single best ...
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34 views

Recent algorithms for correcting mislabeled data using multilayer perceptrons

I am doing literature research on algorithms for correcting mislabeled data using multilayer perceptrons. Found an "old" paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al....
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1answer
50 views

Are the labels updated during training in the algorithm presented in “An algorithm for correcting mislabeled data”?

I am trying to understand an algorithm for correcting mislabeled data in the paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al. The authors are suggesting to update the ...
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1answer
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What are the main differences between skip-gram and continuous bag of words?

The skip-gram and continuous bag of words (CBOW) are two different types of word2vec model. What are the main differences between them? What are the pros and cons of both methods?
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Is there an efficient way of determining the layers with the best performance as feature extractors in GoogleNet?

I am using a caffe model of pre-trained GoogleNet trained on ImageNet from here for image retrieval task (place recognition, more specifically). I would like to know the layer with best performance ...
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TensorFlow to LOW memory usage [migrated]

I'm on an Ubuntu 19.10 machine (with KDE desktop environment) with 8GB of RAM, an i5 8250u and an MX130 gpu (2GB VRAM),running a Jupyter Notebook with tensorflow-gpu I was just training some models to ...
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What are some well-known problems where neural networks don't do very well?

What are some well-known cases, problems or real-world applications where neural networks don't do very well? Specification: I'm looking for specific regression tasks (with accessible data-sets) ...

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