Questions tagged [data-augmentation]

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Why we call Mix-up method is a data augmentation technique?

I am bit confused in the Mixup data augmentation technique, let me explain the problem briefly: What is Mixup For further detail you may refer to original paper . We double or quadruple the data ...
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12 views

In a CNN architecture, is it possible to incorporate both class weights and data augmentation?

I'd like to conduct image classification using some CNN architectures, but the problem is that my classes are imbalanced, and each class has insufficient data. To solve this situation, I have a ...
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23 views

CutMix VS Mixup Data Augumentation method for end-to-end deep learning Traning

I am looking for arguments on which Data augmentation (Mixup VS CutMix) method would be preferable for Image data and Time-series classification data. As for as I know, both have the following ...
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21 views

Augmentation for sound recognition of dog barks for CNNs

I am training CNNs to recognize dog barking, and for this I would like to augment the data sets I have (~30'000 10s clips with either barks, or no-barks in them). The straight forward idea was to mix ...
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1answer
40 views

ValueError: Error when checking input: expected time_distributed_6_input to have 5 dimensions, but got array with shape (32, 224, 224, 3) [closed]

I am trying to apply data augmentation to avoid overffiting in my CNN-LSTM image classification model. My training data has the shape: (1882, 1, 224, 224, 3) My ...
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1answer
22 views

how does zoom out works in data augmentation?

how does zoom out works in data augmentation? I'm reading a doc on data augmentation in Keras ,and it says that randomZoom(0.2) zooms in and out by a factor in rage ...
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2answers
33 views

Removing outliers from a multi-dimensional dataset & Data augmentation

Removing the outliers of a single-dimensional data can be easily done by removing the points that are outside of the IQR range. But how should the process of detecting and removing outliers be done if ...
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20 views

Methods to combine datasets from different time periods

Consider a multivariate time series forecasting task where I have two datasets A and B. A goes from 1960 to 2020 and B goes from 2010 to 2020. There is a feature f ...
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2answers
42 views

How much data augmentation is required on an imbalanced dataset?

Imagine I have a dataset with positive and negative sentences, and I need to train a transformer (Like BERT) to do the binary classification. The problem is that there are 100 negative sentences and ...
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17 views

Data augmentation within epochs vs across epochs

Usually in deep learning data augmentation is applied by creating a new augmented version of each training sample for each epoch. Therefore the amount of training samples for each epoch stays the same ...
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186 views

How do I perform permutation of columns in a pandas dataframe in group of threes

I am relatively new to this platform and python in gerenal. I have got a data frame as follows and I want to implement data augmentation algorithm by permutation. The numbers in the data frame are XYZ ...
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5 views

save reconstructed data points from variational autoencoder as original MNIST

I have a VAE implementation that generates images from the latent distribution. I want to save those "images" as we have in the original dataset. For example, my VAE generates a data point, ...
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1answer
24 views

Batch normalization for multiple datasets?

I am working on a task of generating synthetic data to help the training of my model. This means that the training is performed on synthetic + real data, and tested on real data. I was told that batch ...
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25 views

Specify torchvision transforms depending on the properties of an image and a mask

I have a dataset 1000 of images and corresponding segmentation masks from dermatologists. The images come in different sizes (as low as 400x600 and as large as 4Kx4K). 95% of image pixels are not ...
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22 views

How can I get dark, median-brightness and bright distribution histograms from an image?

I'm trying to replicate an augmentation technique used in this paper. This is how they explain the procedure: [...] the augmentation technique was used to adjust the histogram because the intensity ...
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1answer
94 views

What is the scope of Keras' ImageDataGenerator.flow_from_dataframe seed parameter?

I've been working on a U-Net model using training images stored on my local drive. To load these I have been using Keras' ImageDataGenerator.flow_from_dataframe ...
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50 views

SMOTE for Image regression?

Can you use something like SMOTE for an image regression task, where the target value is very skewed and imbalanced? I already tried using classic augmentation techniques like flipping, cropping, etc. ...
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14 views

Working on an image classification project (microscopic images) , have some doubts [closed]

Currently, I am working on an image classification project. The data set contains very high resolution images taken via an electron microscope. Hence, I have few and limited instances. I have done EDA ...
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1answer
602 views

Why does adding data augmentation decrease training accuracy a tiny bit?

Before data augmentation, my model clearly overfits and hits a 100% training accuracy and a 52% validation accuracy. When only adding data augmentation with Keras, as a regularization technique, it ...
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1answer
73 views

Does Mixup requires two loss functions?

I created a neural network with multi-label classification using MSE. Now, I would like to use Mixup. Do I need two loss functions (for each target one) or is the result the same if I just combine the ...
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1answer
148 views

How to mantain the nested structure of a tf.dataset after applying map?

I'm creating a tf.dataset object containing 2 images as inputs and a mask as target. All of them are 3D in grayscale. After applying a custom map, the shape of the object changes from ...
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1answer
134 views

How to implement random cropping during training?

I'm developing a U-net like model which segments the damaged tissue of the brain between two time-points in Multiple Sclerosis patients. The model is given the baseline and follow-up images as x and ...
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50 views

Data augmentation for tabular data in a multi label classification task

The task at hand is to predict the future lab values for a patient (1 if abnormal and 0 if normal) using the previous numerical data. It is a multi-label, multi-class time series classification task. ...
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1answer
216 views

Rescale parameter in data augmentation

I'm a little bit lost about the rescale parameter in the ImageDatagenerator function. I know that the rescale argument by itself does not augment my data and that ...
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46 views

Balancing on the particular imbalanced classes of image dataset

I have a dataset that has 12 classes in the base directory. However, these 12 classes consist of several amounts of Images. The number of images of 12 classes is inconsistent therefore its impacts the ...
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1answer
496 views

How many images are generated when ImageDataGenerator is used, and when data augmentation is included as a part of the model?

Is there any way to know the number of images generated by the ImageDataGenerator class and loading data using flow_from_directory method? I searched everywhere for the same but couldn't find anything ...
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106 views

Is it possible to increase the number of images of one class using data augmentation, which is not applied on the other class, in the same dataset?

I have 2 classes for my image classification problem, say class A and class B, and I am using tensorflow and keras for the same. One of them have around 5K images while the other have just around 2K ...
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7 views

How do you compare outputs from different augmentations for consistency training?

I'm trying to figure out how papers using consistency train on unsupervised data, and I'm stuck on how outputs from different augmentations are compared. Since the augmentations are transformations, I ...
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29 views

Constraining a Deep Neural Network based on a priori knowledge of a real world system

I am new to this field and to StackExchage, so I guess I'll start by saying hello! I am building a deep neural network to model a physical system which takes a set of inputs based on real-world ...
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2answers
261 views

What is the use of using width/height shift in data augmentation?

I'm not sure to understand the use of augmentation data using width shift and height shift. Say I have limited image data, and I want to create new data using Keras' ImageDataGenerator. To classify ...
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22 views

Should I resize my object localization dataset in order to train a fully convolutional network?

I want to create a fully-convolutional neural net that trains on wider face datasets in order to draw bounding box around faces. The dataset is highly diverse in the image sizes. So my question is, ...
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1answer
36 views

Is it better to augment both training and validation sets or just training set?

Is it better to augment data both training and validation sets or just the training set in order to achieve the best accuracy possible on a convolutional neural network? why?
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1answer
20 views

Textbook definition of Data augmentation

I'm trying to find a textbook definition of Data augmentation. We all know what it is, but I'm having a hard time finding a reliable source which I can cite for my paper: The Wikipedia article on ...
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1answer
340 views

First perform data augmentation or normalization?

Should I first perform data augmentation or normalization in deep learning? I am mainly interested in 2D and 3D input data. In tutorials that I have seen so far the data augmentation always comes ...
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2answers
177 views

Why should I use data augmentation as Keras layer

I've seen in several Tensorflow/Keras tutorials that data augmentation functions are added as keras layers. When I converted my Keras Python model (for production purpose) to TensorflowJS I faced the ...
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1answer
1k views

What exactly are the data augmentation experimental Keras' layers doing?

From what I gathered, data augmentation consists in increasing your number of instances in your dataset by applying some transfromations. Let's say I want to classify images. If I apply a random ...
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1answer
15 views

Are GPTs close to real intelligence or just another Data In -Data Out -Data Permutation and Combinations?

Use cases and Solutions surrounding GPT's have taken NLP world with storm and started the GPT-Best vs GPT- Not So Best war on the internet. There are solutions been derived from API's provided by HF. ...
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1answer
50 views

Is there any possibility to apply deep dreaming in data augmentation?

I looked into the deep dreaming concepts and feel like this has the potential for data generation. But i'm not sure how possible this concept is. Any thoughts regarding this?
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207 views

How to change voice features in python without affecting speech/language features?

I am trying to build a CNN model which should be able to identify the language being spoken in an audio file. I have extracted the MFCC matrix (for 13 coefficients) for each audio file and trained it....
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2answers
114 views

Problem of continuous training - Supervised learning

I am sure this is a most common problem, but would like to know by experts on how to tackle it. Note that, I mostly deal with textual data (NLP problems). When a supervised learning model is created, ...
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2answers
751 views

Why are results without Transfer Learning better than with Transfer Learning?

I developed a neural network for license plate recognition and used the EfficientNet architecture (https://keras.io/api/applications/efficientnet/#efficientnetb0-function) with and without pretrained ...
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241 views

Data augmentation for recommendation systems

I have a user-item matrix that I use to train a denoising autoencoder to predict the top-k items to recommend to the different users. The idea is to corrupt the matrix by erasing a percentage ...
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1answer
67 views

Training with less data

Most problem with machine learning projects I have faced is the lack of data. The samples available are enough to disqualify rule based approach but not enough for a neural network to train. For ...
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167 views

How to train a keras model on both original and augmented data from ImageDataGenerator?

I have a dataset that contains about 87000 images in a directory, with each class in a separate subfolder. I've tried the class ImageDataGenerator() and the ...
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2answers
352 views

Should augmentation also be performed on the validation set when the dataset is imbalanced?

I am training a CNN on images (2 classes) and I have an imbalanced dataset (1:7 ratio). I am trying to tackle this by performing offline image augmentation. Should I perform augmentation also on the ...
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1answer
46 views

What's the difference between keras api augmentation and data augmentation definition?

The augmentation definition is increasing the number of images by using rotation, crop and flip to avoid overfitting. The keras API apply augmentation but no increasing the number of image. What keras ...
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1answer
256 views

How to do data augmentation for Machine Learning on statistical data?

I am training machine learning classification model. I have data in csv format with lets say 5 features(or columns) and 100 such observations(or rows). I want to add more similar data to improve my ...
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152 views

What is the difference between domain randomization and data augmentation?

Domain randomization (https://arxiv.org/abs/1703.06907) is used to create a synthetic dataset with enough variance that it will encompass unseen real data, as just one variation. I am trying to ...
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118 views

Problem with data augmentation only on train set!

So I want to augment my data but unfortunately, I don't have an individual folder for train and validation set, and of course, I want to only augment the train set but if I use this: ...
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1answer
1k views

Data augmentation for multiple output heads in Keras

I have a transfer learning based two output classification problem. So, accordingly, I have formatted my data to have X_train as a ...