Questions tagged [data-augmentation]

Data augmentation is the practice of making slight modifications to the observed data with the goal of making models trained on that data more robust.

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Should training set images change during epochs?

I'm training a convolutional neural networks for image segmentation. In training data preprocessing i'm applying some data augmentation to change luminosity of images. I'm using tensorflow ...
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is this way of applying data augmentation correct [closed]

I'm training a CNN and want to apply some data augmentation to my input images. I combined some code from tensorflow tutorials and have the following workflow: I have a dataset containing all ...
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Structure of Generative Adversarial Networks (GAN) for mapping a simulation model

There is a simulation model of a system that I want to map as a neural network to test if a better execution time can be achieved with similar accuracy. The simulation model receives real-valued ...
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Is GAN effective enough to replace data augmentation and manual annotation?

We all know that GAN can be used to augment and expand our dataset Can a GAN be used for data augmentation?. But my question is, is it effective and fast enough? For example I have done experiment ...
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How does Data Augmentation work for supervised learning models?

I've ran into a few Kaggle competitions where the winning solution used data augmentation, and a new ML platform, which claimed to help with Data Augmentation. Use cases were imbalanced classes and ...
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Can GANs be used for timeseries data augmentation? (2019)

Timeseries, in particular signal timeseries, are distinct in many respects - so GANs working on images may not work for timeseries. Since other questions asking on data augmentation, GANs have ...
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Domain-Agnostic Data Augmentation Techniques?

I was wondering if there are any accurate and widely accepted domain-agnostic data augmentation techniques? Usually they are highly contingent on the data input type (text, images, time-series, ...
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Needing 4th dimension for shape [closed]

I was working on a transfer learning solution to categorize between diseases in the eye. I was using the Xception model built into Keras and it uses a data set that I was able to accumulate. However ...
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GANs for non image data

I'm looking to narrow down the subject for my bachelor thesis: I am currently working on a project, that only offers a small dataset and there will be no more data incoming for now. What I'm trying to ...
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Data augmentation for animal measurements

I am looking for methods to do data augmentation for an animal study. What I want to do is generate a large number of data points by using a min and max measurements of the animal. So I have 2 ...
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Variation in accuracy of data splitting before and after data augmentation

How much accuracy of the system varied/changes between two cases Data augmentation before splitting Data augmentation after splitting, only on training data Is there any literature published?
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Data augmentation on entire dataset before splitting

If I apply rotation of 5 different angles and randomly cropp 10 different images from each rotated image and than divided into training testing and validation. Will it be totally incorrect evaluation ...
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Data augmentation techniques for numeric datasets? [duplicate]

I'm writing a paper about Data Augmentation and I'm looking for some way of increasing the size of a dataset. I'm already aware of the techniques used for images (transformations, PCA, blurs, etc.) ...
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Why is using keras ImageDataGenerator for data augmentation relevent?

I have used keras ImageDataGenerator to generate more data in my neural networks as I have had really small datasets and it has proven itself. As far as I ...
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Augmentation of data collected from single stationary source

How do one augment data that is being collected from single stationary sensor source. The orientation, color and size always remain same. Only the pattern in the dataset vary (example : sunspots are ...
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261 views

Can upsampling be used as time-series data augmentation?

I have a timeseries with a sample every 5 minutes. I want to forecast the timeseries multiple step ahead (e.g., 60 minutes, which is 12 samples ahead) using its past values. Unfortunately my model ...
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141 views

Augmented data in test/val set

I am about to build CNN for image classification. I have a rather small dataset and have done some data augmentation to make it bigger. While doing so, I got a little confused whether what I am doing ...
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264 views

Does online data augmentation make sense?

Data augmentation is popularly done online as that is how it is typically implemented and suggested in neural network frameworks like Keras and TensorFlow. I have also seen it described in e.g. the ...
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How to choose data augmenation technique?

How can I evaluate the performances of a data augmentation technique? Shall I use the improvement in accuracy? Can a data augmentation technique improve performances of a model, for instance a ...
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augment / map / forecast a time delayed time-series

I am not sure what the proper title for my question is but I'll try to describe what I want to do. I have a time-series where the value changes over time, meaning a portion get's added time-delayed ...
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Subsampling as a method for time series train/validation splits

I have a question concerning train-test splits for time series data: Background I have a dataset of sensor data points for 13 month with datapoints measured every 5 minutes which I downsample to ...
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What are some techniques to augment tabular data?

As we know we can perform data augmentation to "image dataset". We can apply random rotation, shifts, shear and flips over images. Are there techniques to augment tabular small dataset? I know the ...
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Image Augmentation or incrementing dataset by flipping/mirroring?

My task is a regression task, where an input image results in another, transformed image. So far so good, works quite well. As my data set is fairly small, I want to take some actions. Here I wanted ...
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When doing data augmentation, should you train with the original data as well or just the augmented data?

When doing data augmentation in computer vision problems, should you train with the original (un-augmented) data as well or just the augmented data? Are there pros and cons to the two strategies or ...
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Adding Bad Features to Decrease Model Performance

I have a dataset on which some researchers have already performed some definitive data analysis and feature selection. Fitting a model to this dataset returns pretty good accuracy. In order to ...
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Data augmentation methods for Raman Spectra

I'm building a CNN model based on Raman spectroscopy data and I wanted to experiment with data augmentation. What would be some reasonable techniques to try? I have found this paper which suggests ...
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Data Augmentation Techniques for Cat/Binary/Continuous Numerical Dataset

I am using the bank marketing dataset from the UCI ML repo to build an example of a big data storage system along with ETL workflows and Machine Learning models. I would like to create more data so I ...
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How to paraphrase and augment training data for a question answering ML model?

I have only 50 question, answer pairs in my training data, where each question represent a unique intent. However, the training data is too small to build any meaningful ML model. What are the ...
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Imputing nested time series data with R

Does anyone know what is the superior algorithm to impute data in time series? I had strong dropouts over time because it was free to participants how many times to participate in my study (otherwise ...
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Active learning for object detection- Batch Selection

I have a small dataset of about 220 images for three classes. I am using YOLO (you only look once) network for an object detection. I am trying to use Active learning in order to reduce the number of ...
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Using bootstrap for robust estimation

I am hoping to understand the process of bootstrapping outlier-contaminated data, and the effects on (simple) OLS estimators. In particular, we have a DGP, $$Y_t = \beta X_t + \epsilon_t$$ where $\...
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How do you link multiple X input images to a single ground truth image in machine learning?

What I'm asking is basically manual data augmentation. I have some very specific data augmentation for my inputs so I have to create them first in another software instead of doing it on the fly. I ...
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Can a GAN be used for data augmentation?

Can a generative adversarial network (GAN) be used for data augmentation (i.e. to generate synthetic examples that are added to a dataset)? Would it have any impact on the performance of a model ...
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181 views

Why data augmentation techniques are applied stochastically

I am using object detection in order to detect objects from drones. I have noticed that using data augmentation can create some images of object as if they were recorded from a different position of ...
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Gaussian Mixture Division

In the study of probabilistic graphical models (PGMs), the loopy belief update propagation (LBUP) message passing algorithm requires the division of unnormalised probability distributions. If the ...
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Data Augmentation and Balancing Dataset in a context of Object Detection

I have a dataset of object detection (bounding box + class) with 2 classes (excluding "background" class). I am worried about two things : First, my dataset counts only 196 samples (I am not too ...
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Data Augmentation strategies for Time Series Forecasting

I'm considering two strategies to do "data augmentation" on time-series forecasting. First, a little bit of background. A predictor $P$ to forecast the next step of a time-series $\lbrace A_i\rbrace$ ...
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Data augmentation on training set only?

Is it common practice to apply data augmentation to training set only, or to both training and test sets?
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Data augmentation and effective class imbalance

Let's say I have a binary classification dataset skewed towards negative samples. Let's say it's 1000 positives and 100000 negatives. Let's say it's image data. I'm training a classifier and I'm ...
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How to augment imaging data for deep learning if I have subject meta information about the original images?

I'd like to try deep learning on about 200 CTs of cancer patients and I gave a lot of meta information about these people that I'd like to make use of in the classification (age, body mass index, ...
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280 views

ConvNet data augmentation, full pass or random samples?

I'm adding data augmentation on a FCN model, right now I'm doing simple flips, random zoom and random rotations. At the moment for each sample I do all the four transforms (vertical flip, horizontal ...
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Artificial Datasets; useful for Ai purposes, what about Ai applications in other fields of science?

In Artificial Intelligence, it's common to create sample 'fake' datasets and use them for the purpose of making more efficient algorithms from classification to regression. Datasets with data points ...
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A Bayesian model using random-walk Metropolis method for data augmentation

I have following model, z=beta1+X1*beta2+e; e~N(0,sigma2) prob=exp(z)/(1+exp(z)); and y= 1 with prob, 0 with (1-prob). I have the following prior: beta~N(betahat,A^(-1)); betahat=c(0,0) and A=0....
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Why is data augmentation classified as a type of regularization?

In deep learning papers, data augmentation is often presented as a type of regularization. For example, this is explored in Chiyan Zhang and coauthor's presentation at ICLR17, Understanding deep ...
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Image classification: Using image augmentation to resolve class imbalance

I am working on an image based classification task with some significant class imbalance in the training database of images (largest class: 4967 images, smallest class: 61 images). I will be ...
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Convolution Neural Network Data Augmentation After Normalization Works Much Better [closed]

I am training a Convolution Neural Network similar to LeNet5 to detect road signs in the German Traffic Signs Dataset. With about 35,000 training samples I get to 95% validation accuracy. To improve ...
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Data augmentation

In many papers on CNN,I have read that data augmentation is carried out on a per epoch basis. My thoughts regarding this were that data augmentation is carried out prior to starting the training ...
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Why does the Ciphar 10 tutorial on TensorFlow crop the images to be 24x24?

I was going over the cifar 10 tutorial in tensorflow and was trying to understand why the guys in tensorflow/google decided to crop the images. The only reason I could justify it to myself is because ...
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How does data augmentation reduce overfitting?

I'm trying to understant the benefit apported by the step of data augmentation in a classification algorithm. I have a vector of hexadecimal strings and a column vector containing the label ...
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Data augmentation step in Krizhevsky et al. paper

In the paper Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012., ...