Top 5 Interview Questions on Autoencoders

This article data science blogthon.

Source: DDI

prologue

An autoencoder is an unsupervised model that takes unlabeled data and learns to code effectively on data structures that can be applied in different contexts. It fits a function that maps the data from the input space to coordinates of lower dimension, and then to the same dimension of the input space to minimize loss.

Autoencoders can be used for various tasks such as anomaly detection, image compression, image retrieval, and image denoising. Therefore, given its popularity and widespread use in the industry, a clear understanding of this is essential to its success. Data science interview.

In this article, we’ve compiled a list of five important questions about Autoencoder. Use this as a guide to familiarize yourself with the topic and craft effective answers to help you succeed in your next interview.

Interview questions about autoencoders

Question 1: What is an autoencoder?

Answer: An autoencoder is a neural network whose purpose is to learn an identity function to reconstruct the original input while simultaneously compressing the data in the process. The reconstructed image is an approximation of the input x.

The concept first appeared in the 1980s, followed by seminal research papers by Hinton and Sarakhdinov in 2006.

Figure 1: Diagram showing the architecture of the autoencoder

Source: Lillian Wen

It consists of two networks:

i) Encoder network: An encoder network transforms the original high-dimensional input data into a potential low-dimensional or compressed representation. Therefore the input size is larger than the output size. In other words, the encoder learns to create a compressed/encoded version of the input data, achieving dimensionality reduction.

ii) Decoder network: The decoder network recovers the data from its latent representation, and the original input data to the encoder is nearly identical to the reconstructed input. That is, the decoder reconstructs the original data from the compressed version.

Figure 2: Diagram showing compression and decompression/reconstruction of the original image

Source: programmatically.com

The encoder function g(.) is parameterized by ϕ and the decoder function f(.) is parameterized by θ. The trained low-dimensional code for input x of the bottleneck layer is z=gφ(x) and the reconstructed input is x′=fθ(gφ(x)).

The parameters (θ, φ) are learned together to output a reconstructed data sample identical to the original input x≈fθ(gφ(x)). Metrics such as cross-entropy and MSE loss can be used to quantify the difference between the two vectors.

Internally, the bottleneck consists of a hidden layer that writes code that represents the input. Hidden layers usually have fewer nodes than the input and output layers and prevent the network from learning the identity function. Having fewer nodes in the hidden layer than input nodes forces the autoencoder to prioritize useful features that it wants to keep and ignore noise.

This architecture is useful for applications such as dimensionality reduction and file compression. These applications store memory-efficient versions of the data or reconstruct less noisy copies (versions) of the input than the original data.

Autoencoders are learned when compared to deterministic methods of data compression. That is, it relies on features specific/unique to the data the autoencoder was trained on.

Note: For dimensionality reduction to be effective, low-dimensional features must have some relationship to each other. The higher the correlation, the better the reconstruction.

Question 2: What if both hidden and input layers have the same number of nodes?

Answer: If the hidden and input layers have the same number of nodes, the encoding will be similar to the input, making the autoencoder completely useless.

Question 3: Are autoencoders and PCAs the same or is there a difference?

answer: No, autoencoders and PCAs are not the same thing.

I haven’t covered PCA in detail in this post, so before I discuss the differences between PCA and autoencoders, I’ll give a quick explanation.

Principal Component Analysis (PCA) is a method of projecting/transforming high-dimensional data into a low-dimensional space while preserving as much information as possible. The variance of the data determines the vector of projections. By limiting the dimensions to a certain number. A dimensionality reduction is achieved for the components that make up most of the variance of the dataset.

As for comparing PCA and autoencoders, here are the differences:

1. Principal component analysis (PCA) and autoencoders (encoder networks) achieve dimensionality reduction. However, autoencoders are more adaptable.
2. PCAs can only model linear functions, while autoencoders can model complex linear and nonlinear functions.
3. The PCA features are linearly uncorrelated with each other because the features are orthogonal-based projections. However, auto-encoded features may be correlated because they were trained for approximate reconstruction.
4. Compared to autoencoders, PCA is fast and computationally less expensive.
5. An autoencoder with a single hidden layer and linear activation function is similar to PCA.
6. With so many parameters, autoencoders are susceptible to overfitting. (However, regularization and careful design choices can prevent this)

Question 4: When should I use PCA and when should I use autoencoder?

answer: In addition to considering computing resources, the choice of technique depends on the properties of the feature space itself. If the features have nonlinear relationships, autoencoders can compress the data/information into a lower-dimensional latent space with the ability to model complex nonlinear functions.

Figure 3: Diagram showing the output of PCA and autoencoder when different 2D functions are applied

Source: Urwa Muaz

Figure 4: A diagram showing the output when the PCA and autoencoder are subjected to different 3D functions

Source: Urwa Muaz

Figure 5: Diagram showing output when PCA and autoencoder are exposed to different random functions

Source: Urwa Muaz

Therefore, it is clear from the figure above and MSE that Autoencoder reconstructs more accurately whenever there is a non-linear relationship in the feature space. Conversely, PCA preserves only the projection onto the first principal component and loses all other information perpendicular to it.

Question 5: List some applications of Autoencoder.

Answer: Below are some of the applications of autoencoders.

1. Dimensionality reduction: An encoder network (of autoencoders) learns to create a compressed/encoded version of the input data, achieving dimensionality reduction.
2. Feature extraction: Given unlabeled data, autoencoders can efficiently encode the structure of the data and use that information for supervised learning tasks.
3. Image noise reduction: An autoencoder takes a noisy input image as input and reconstructs a noiseless output by minimizing the reconstruction loss from the original goal output (noiseless). The trained autoencoder weights can be used to denoise the original image.
4. Image compression: Autoencoders aim to compress data in the process while simultaneously learning an identity function that reconstructs the original input. The reconstructed image is an approximation of the input x.
5. Image search: Image databases can be compressed using an autoencoder. Compressed embeds can be used to compare or search using encoded versions of search images.
6. Anomaly detection: Anomaly detection models can detect fraudulent transactions or imbalanced monitored tasks.
7. Missing value imputation: Missing values ​​in the dataset can be imputed using a denoising autoencoder.

Conclusion

In this article, I’ll share the five most important interview questions about autoencoders that you might be asked in a data science interview. You can use these interview questions to work on your understanding of various concepts and create effective answers to present to your interviewer.

In summary, the main points of this article are:

1. Autoencoders aim to learn the identity function to reconstruct the original input while simultaneously compressing the data in the process. The reconstructed image is an approximation of the input x.

2. An autoencoder consists of a network of encoders and decoders.

3. Internally, the bottleneck consists of a hidden layer that writes code that represents the input. Hidden layers usually have fewer nodes than the input and output layers and prevent the network from learning the identity function.

4. If the hidden and input layers have the same number of nodes, the autoencoder is useless.

5. Both PCA and Autoencoder (Encoder Network) achieve dimensionality reduction. However, autoencoders are more flexible. PCAs can only model linear functions, while autoencoders can model complex linear and nonlinear functions.

6. Autoencoders can be used for dimensionality reduction, image retrieval, image compression, missing value imputation, etc.

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