Data Augmentation: Gaussian Noise

Computer Vision

Difficulty: 3 | Problem written by zeyad_omar
Problem reported in interviews at


In deep learning, the learning process may turn to memorizing. This is known as overfitting, where the model gives high accuracy for the data in which the model was trained but gives terrible accuracy for the unseen data.

One of the techniques used to address this issue is the introduction of some random noise into the dataset to help the model become more generalized.

In this problem you are given an image and the mean and standard deviation of the noise and you are asked to return the image + noise.

*** Please use  np.random.seed(10) to match our test cases.

Sample Input:
<class 'list'>
x: [[1.31, 4.2, 3, 5], [1, 0, 1, 0], [1, 1, 1, 1]]
<class 'int'>
mean: 0
<class 'int'>
sigma: 1

Expected Output:
<class 'numpy.ndarray'>
[[ 2.6415865 4.91527897 1.45459971 4.99161615] [ 1.62133597 -0.72008556 1.26551159 0.10854853] [ 1.00429143 0.82539979 1.43302619 2.20303737]]

This is a premium problem, to view more details of this problem please sign up for MLPro Premium. MLPro premium offers access to actual machine learning and data science interview questions and coding challenges commonly asked at tech companies all over the world

MLPro Premium also allows you to access all our high quality MCQs which are not available on the free tier.

Not able to solve a problem? MLPro premium brings you access to solutions for all problems available on MLPro

Get access to Premium only exclusive educational content available to only Premium users.

Have an issue, the MLPro support team is available 24X7 to Premium users.

This is a premium feature.
To access this and other such features, click on upgrade below.

Log in to post a comment


Input Test Case

Please enter only one test case at a time
numpy has been already imported as np (import numpy as np)