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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trying to create a cache dataset in monai: LinAlgError: SVD did not converge [closed]
This is the error that I am getting:
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How useful is data augmentation techique?
I have a very small sample size in one group and cannot perform statistical analysis. Is there any other way of performing analysis by artificially generating data and then presenting the results of ...
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Computing coordinates of points of an image after elastic deformation
My task is: given an image and set of points of interest, elastically and randomly deform the image and save it with the modified aforementioned points.
example: (blue points are the points of ...
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How does the order of implementation of Generative Data Augmentation and Generative Audio Super-Resoln matter? Which one should be implemented first?
I have a low quality audio dataset that I will use for classification. My goal is both to increase the quality of this dataset to make it easier to label it with supervised methods (super resolution) ...
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Regression ML model: Data Augmentation [closed]
I'm currently working on data augmentation to my regression problem, and a (possible) solution that came to my mind was to add a perturbed dataset to the original dataset, and hence double the ...
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When is the case that a classifier's accuracy is better when no data augmentation has been applied?
When is the case that a classifier's accuracy is better when no data augmentation has been applied (Compared to the dataset with the augmentations applied)? I'd like to know especially how the data is ...
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Data augmentation to equilibrate class occurences on image set with multiple different objects in each image
I am using this (reduced) dataset to run tests for detection of defects in solid wood using YOLOV5. Initial tests are promising. As can be seen in Fig. 1, my class occurences are quite imbalanced - ...
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Investigating the Impact of Additive Gaussian Noise on EEG Signal Classification: Analyzing the Relationship between Augmented and Original Data
Definition: I have conducted research on EEG signal classification, specifically focusing on distinguishing between two different classes using raw EEG signals. Data availability poses a significant ...
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small dataset for a regression task
I am trying to train a NN model in MATLAB to predict the amount of overflow for flooded junctions in an urban runoff system and I have 45 samples and 15 features. The issue is, I don't think 45 ...
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Data augmentation specific per class
I have a database of defects on plastic films. Defects are burns, holes, and similar things. Some defects are direction specific. A vertical sign on the material represents a scratch done on the ...
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Augmenting Image Data on Small Dataset [duplicate]
I want to train image segmentation model using U-Net with pretrained ResNet34 as encoder. My dataset is really small, i separate it with
Train data : 57 images
Validation data : 16 images
Test data : ...
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How to perform MCMC posterior sampling of $X+Y$ random variable?
Let $X$ follow normal distribution with mean $\mu_X=10$ and variance $V_X=20$. Let $Y$ follow truncated normal distribution with mean $\mu_Y=-10,V_Y=8$, where $Y\leq 0$ by truncation.
Let $Z=X+Y$. ...
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Manually adding edge-cases to a text classification model
Suppose I want to get training data for a model that deals with sentiment analysis for text that indicates an affirmative (yes) or negative (no) response, such as
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Were missing values in data augmentation procedure parameters?
Suppose I have a data set with $(Y,X)$ where $Y$ has some missing entries(say 20% missing). From $p(Y,X|\lambda)$ and prior $p(\lambda)$, I can conduct MCMC by data augmentation to impute missing $Y$'...
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Monte Carlo Options for Data Augmentation
In the seminal paper by Tanner and Wong (1987) on data augmentation, they describe a method for obtaining the posterior distribution $p(\theta|y)$ by data augmentation. Let $Z$ be a latent variable (...
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Can the same training data be used for MLM and fine-tuning of a transformer model?
Can I use the same training and validation data to perform MLM and train the weights of a classification head?
Here is the background of my specific problem:
The problem is a binary classification ...
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What is the correct method for training NLP models with augmented data?
I have a very small dataset (~50 rows) for a text classification problem. I found some open source data that's similar to the problem I'm trying to solve.
Should I...
Train the (BERT) model on the ...
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Does image data augmentation make sense when fine-tuning a transformer-based encoder-decoder model (Donut) on a small dataset (~100 samples)?
I am trying to fine-tune Donut model on ~100 (training) labelled data samples (pairs of images and json files). (Donut is a transformer-based encoder-decoder model, the encoder encodes images and the ...
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Synthetic vs Data augmentation for low dimensionality data
I have problems understanding data augmentation. I currently have low-dimension features, each data point only has 3 features. A total of 20k non-linear data with only 3 features. I have generated ...
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Augumentation methods for audio clips (RAVDESS dataset, only audio)
I'm trying to augment the data of the RAVDESS dataset. I still performed many techniques over the data such as:
pitch tuning
stretching
shifting
noise adding
do you know any other way to augment the ...
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Classifier as loss function for image generation, "tricked" too easily
I have a StyleGAN model for which I want to manipulate some properties of the generated image, e.g. eyes being opened/closed [1].
I finetuned a classifier (using pretrained ConvNeXt) on subset of 500 ...
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Calculating Shannon Information of Data Augmentation Strategies [closed]
I recently caught Andrew Ng's 2021 talk on MLOps (MLOps: From Model-centric to Data-centric AI). At 26:40, he talks about calculating the effectiveness of cleaning your data (training examples) vs. ...
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Decide on the number of adversarial samples to include during training
A sample is considered adversarial if it drastically changes classifier's confidence $\in [0,1]$ when given as input. For example, if a spam/ham binary classifier considers some input $X$ to be 0.9 ...
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Does it make sense to use data augmentation on the Validation set? (note, this is not the same as asking to augment the test set)
Curious, do people use data augmentation on the validation set? I am aware there is a debate for the test set -- but the validation set is usually a split form the train set, so wouldn't it make sense ...
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How to perform data augmentation with traditional machine learning algorithms?
I am currently working on a multi-class image classification project, in which I have to use traditional machine learning and feature extraction methods (no convolutional neural networks).
I know data ...
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How do we perform hyperparameter tuning on parameter of data augmentation?
I was wondering how do we perform hyperparameter tuning on parameters of data augmentation. Suppose I have to select the best pair of (alpha, alpha) values of beta distribution that works best on data ...
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Is it "cheating" to augment data by adding noise to the label?
I have a data set that I'd like to augment by adding noise to the label. I've seen people on Kaggle duplicate each row twice and add 1 and subtract 1 to the label
Whenever I do this and then do a ...
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ROSE acceptable dispersion/shrinkage
To solve imbalanced data, I used oversampling strategy using ROSE algorithm in Python. As you may know, ROSE is a smoothed bootstrapping method and we can control the dispersion of the augmented data.
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Does data augmentation with white noise improve accuracy of deep learning models?
I was reading Aurélien Géron's Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow. There, on the 14th chapter I read something on data augmentation which I could not be sure of its ...
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What is the equivalent of image augmentation in time series forecasting? I'm in need for more data [duplicate]
I think it would be fair for me to explain a little bit on a background into what I am doing so that my question would make more sense.
I am currently working at a company where I need to develop a ...
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Overfitting, generalization, data augmentation, regularization, how do they relate to each other? How to measure?
Recent work such as Deep Double Descent shows that overfitting is not really a problem with large models, even without any data augmentation or regularization (L2 weight norm, dropout or so).
Edit: Ok,...
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Using data augmentation for balancing dataset
Hey there,
I have a question about a topic that's been discussed many times before, but to which I could find a satisfying answer.
I'm working with a self generated dataset that is comprised of only ...
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When augmenting data, shall the dataset keep a balanced ratio
When training a model it is more and more common to augment data.
posts indicate that only the training set shall be augmented.
On the other hand it is common to split dataset in a fashion following ...
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Based on Data Augmentation in numerical dataset
I want to develop a machine learning model for a dataset like 5 inputs and 1 output. I stuck in stage with not sufficient dataset, I am aware about data augmentation technique in image and text type ...
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Conditional distribution of the weight of a mixture gaussian with data augmentation using gibbs sampling
This question is relate to Differenciate between two distributions using gibbs sampling .
For $t=1,\,\dots,\,n$, let's $r_t\sim\mathcal{N}(0,\,\sigma_t^2)$ and $$\sigma_t^2=\left\{\begin{array}{lcl}
\...
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Why Accuracy increase only 1% after data augmentation NLP?
i have small dataset 4840 samples (60% negative ,28% positive,12% negative) i use data augmentation on training set (70%train 30% test) and i have about 2000 samples for each class while test is ...
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From 1 to 5-shot learning with data augmentation
I'm currently exploring k-shot classification and I would like to start a first experiment on logo classification.
The problem is that for some logos I can only find one image, while for others I ...
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Mask for image padding in semantic segmentation
I'm using data augmentation for a semantic segmentation task, where some images are cropped or rotated. As a result, some padding is added to ensure that the image is always the same size. These ...
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Does oversampling lead to more overfitting than classweights for really small classes?
Assume I have a couple of thousand hens that I want to classify into those that never lay an egg and those that will at some point in their life lay an egg. Assume that already works perfectly.
Now ...
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Data augmentation for traditional machine learning algorithms
Data augmentation suffices multiple purposes, I would list a few here:
Increasing dataset size: The data is just fragment/stand-in trying to represent reality, having more data should thus result in ...
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Learning Distribution of Data [duplicate]
Sometimes it's important to generate data due to data imbalance issues. I heard that data augmentation by leaning distribution of data is a hot topic now. Could you please give me some resources and ...
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How to generate synthetic data from a balanced dataset?
Let say I have a balanced dataset that has a small training sample size (lack of data).
How do I increase the training sample size by generating synthetic data based on the original data?
I believe ...
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What is the best practice to overcome small insufficient data
I have a small number of images (i.e. 108), and I wish to train a deep convolutional neural network with it.
As I know - you need to have a large number of samples to be able to train a neural network,...
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In a parametric model, if I do not have enough data, can I estimate the parameter, and simulate data from the estimated model and estimate again?
Suppose I have a logistic regression model $Y_i=\mathbf{1}(X_i\beta>\epsilon_i)$ to estimate, where the distribution of $\epsilon_i$ is known, $X_i$ follows distribution $F_{\theta}$ with an ...
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Expert Knowledge Acquisition and Machine learning
Having data sets regarding symptoms and diseases such that I use to observe the conditional distributions P(Disease X | Symptom A , Symptom H , Age >20 ) which I use for classification and ...
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Data augmentation by adding noise in python regression model
I am building a regression model for a target variable which is heavy tailed. I want to augment data so that the model gets enough training samples in the region where it's a long tail. Accuracy of ...
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Train test validation splits and augmentation
I am dealing with an image dataset of 400, and split it into 70% train, 15%test, 15%validation. I would like to do some data augmentation (rotations/flips) to increase the amount of train data I have ...
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Data Augmentation causing test and validation sets to be smaller
I am dealing with an image dataset of 400, and split it into 70% train, 15%test, 15%validation. I would like to do some data augmentation (rotations/flips) to increase the amount of train data I have ...
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Augmenting training data with cases that won't be in future data
Background: I am working on coding survey responses, where the respondent writes in a description of their job. So the person might write in "McDonald's Employee" and get coded to something like 1002 ...
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Test Time Augmentation on Validation set?
In the traditional usage of data augmentations, we augment only the train set examples, in order to keep the distribution of the validation and test set equal.
In the TTA method, we apply ...
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