Interview Questions on Autoencoders

In this article, we have presented most important Interview Questions on Autoencoders.

Multiple Choice Questions
1. How many layers are there in Autoencoder?

1. 2
2. 3
3. 4
4. 5
   Ans: 3
   An autoencoder consists of three layers:

1. Encoder
2. Code
3. Decoder

A feed forwarding mesh is created by fully integrating the encoder and decoder; the code functions as a single layer with independent dimensions. The number of nodes in the core layer is a hyperparameter that must be set in order to create an autoencoder. The output network of the decoder is a more precise mirror copy of the input encoder. The coding layer is the only source from which the decoder draws the desired output.

2. Select the correct option.
A. Supervised learning methods include autoencoders.
B. The output and input of the autoencoder are identical.

1. Both the statements are TRUE.
2. Statement A is TRUE, but statement B is FALSE.
3. Statement A is FALSE, but statement B is TRUE.
4. Both the statements are FALSE.

Ans: Both the statements are FALSE.
Simple input of the raw input data is all that is required to train an autoencoder. Since they don't require specific labels to train on, autoencoders are thought of as an unsupervised learning technique. The fact that they create their own labels from the training data, however, makes them self-supervised.
The autoencoder will produce an output that is near to the input but not an exact replica of it. They are not the best option if lossless compression is what you seek.

3. Select the correct option about Denoising autoencoders.
A. The loss is between the original input and the reconstruction from a noisy version of the input.
B. Denoising autoencoders can be used as a tool for feature extraction.

1. Both the statements are TRUE.
2. Statement A is TRUE, but statement B is FALSE.
3. Statement A is FALSE, but statement B is TRUE.
4. Both the statements are FALSE

Ans: Both the statements are TRUE
The denoising encourages the encoder to retain crucial input data while discarding irrelevant data. The hidden representation can then be seen as preserving relevant input features.

4. Select the correct option about Sparse autoencoders.
A. Sparse autoencoders introduces information bottleneck by reducing the number of nodes
at hidden layers.
B. The idea is to encourage network to learn an encoding and decoding which only relies on
activating a small number of neurons.

1. Both the statements are TRUE.
2. Statement A is TRUE, but statement B is FALSE.
3. Statement A is FALSE, but statement B is TRUE.
4. Both the statements are FALSE.

Ans: Statement A is FALSE, but statement B is TRUE.
Sparse autoencoders cause a bottleneck in the flow of information without reducing the number of nodes in the buried layers. It stimulates the network to develop encoding and decoding techniques that only require a minimal number of neurons to be activated. We typically regularize a network's weights, not its activations, so it's interesting that this is a distinct technique.

5. Autoencoders are capable of learning nonlinear manifolds (a continuous, non-intersecting surface.)

1. TRUE
2. FALSE

Ans: TRUE
Manifold learning is an approach in machine learning that assumes that data lies on a manifold of a much lower dimension. These manifolds can be linear or non-linear. Thus, the area tries to project the data from high-dimension space to a low dimension. For example, principle component analysis (PCA) is an example of linear manifold learning whereas an autoencoder is a non-linear dimensionality reduction (NDR) with the ability to learn non-linear manifolds in low dimensions.

6. Autoencoders are trained using _.

1. Feed Forward
2. Reconstruction
3. Back Propagation
4. They do not require Training

Ans: Back Propagation
A popular algorithm for training feedforward neural networks is backpropagation. Instead of crudely computing the gradient with respect to each individual weight, it efficiently computes the gradient of the loss function with respect to the network weights. Gradient methods, including variations like gradient descent or stochastic gradient descent, are frequently used to train multi-layer networks and update weights to reduce loss due to this efficiency.
In order to avoid duplicating computation of intermediate terms in the chain rule, the backpropagation method calculates the gradient of the loss function with respect to each weight using the chain rule, layer by layer, and iterating backward from the last layer.
A machine learning algorithm known as an artificial neural network (ANN), which uses backpropagation and sets the target values to equal the input values, is used in an autoencoder.

7. De-noising and Contractive are examples of .

1. Shallow Neural Networks
2. Autoencoders
3. Convolution Neural Networks
4. Recurrent Neural Networks

Ans: Autoencoders
Autoencoders include de-noising and contractive. A de-noising autoencoder can recreate data from a damaged input signal. The removal of some elements of the original data is an example of corruption. An encoder’s output is usually a refined version of the original input. An encoder’s output is usually a refined version of the original input. An unsupervised learning approach used to train deep networks is a contractive autoencoder.

8. Autoencoders cannot be used for Dimensionality Reduction.
Select the correct answer from below given options

1. True
2. False

Ans: False
The hidden layer has less dimensions than the input and output, therefore it contains compressed information from the input layer, which is why it functions as a dimension reduction for the original input.

9. Autoencoders are trained without supervision.

1. True
2. False

Ans: True
A data compression approach called "autoencoding" uses separate functions for compression and decompression.
a) lossy
(b) Data-specific
c) automatically picked up knowledge from examples rather than being created by humans. As a result, Autoencoder are trained without supervision. Additionally, neural networks are employed to accomplish the compression and decompression operations in practically all settings where the word "autoencoder" is used.
It would be incredibly challenging to compress the input features and then reconstruct them if they were all independent of one another.

Short Questions

1. Define Autoencoders.
A machine learning algorithm known as an artificial neural network (ANN), which uses backpropagation and sets the target values to equal the input values, is used in an autoencoder. In order to recreate the actual input, it is constructed in a way that can do both data encoding and data decoding tasks.

2. How Do Autoencoders Function?
It utilizes the following elements to do the aforementioned tasks:

1. Encoder: The encoder layer compresses the input image into a smaller representation. The original image's distortion is clearly seen in the compressed version.
2. Code: This portion of the network only represents the decoder's compressed input.
3. Decoder: Using a lossy reconstruction and the latent space representation, this decoder layer restores the encoded image to its original dimension.

3. What are the Uses of Autoencoders?
Today's world of images requires the usage of autoencoders for a variety of purposes. These are some of the uses for them:

1. Data compression
2. Dimensionality reduction
3. Image denoising
4. Feature extraction
5. Removing watermarks from Images

4. Give Two Actual Case Studies Where Autoencoders Have Been Used.
Image coloring: Any black and white images are transformed into colored images by autoencoders. As a result, the color can be determined based on the subject of the image.
Feature variation: In this case, noise or unneeded interruptions are eliminated and just the relevant features of a picture are retrieved and employed to produce the result.

5. Describe the sparse constraint.
A sparse constraint is one that appears in the loss function of a sparse encoder. When we set the many nodes in the hidden layer, the sparse constraint ensures that the autoencoder is not overfitting the training data.

6. What is a Bottleneck, and Why is it Used?
The layer between the encoder and the decoder is a bottleneck. Choosing which features of observed data are important and which can be ignored is a well-designed strategy.
It accomplishes this by striking a balance between two factors:

1. Compressibility is a measurement of representational compactness.
2. Some behaviorally important input variables are retained.

7. Name some of the Autoencoder Variations?
Some of the Autoencoder Variations are as follows:

1. Convolutional Autoencoders
2. Sparse Autoencoders
3. Deep Autoencoders
4. Contractive Autoencoders

8. What distinguishes GANs from auto-encoders?
Both the encoding network and the decoding network are simultaneously learned by an autoencoder.The encoder attempts to reduce the input dimensions to a severely compressed encoded form when an input (such as an image) is provided.The decoder is then fed this. The loss measure rises with the difference between the input and output image, therefore the neural network learns this encoding/decoding. After each iteration, the encoder becomes a little bit more adept at locating an effective compressed version of the input data. Additionally, the decoder becomes slightly more adept at reassembling the input from the encoded form.
A generator in generative adversarial networks (GANs) turns a noisy signal into a target space (for example, with images). The discriminator, on the other hand, separates the genuine images that were taken from the desired target space from the false images that the generator produced, making it the other component (the adversary). In order to train the network, two phases are alternated, each with a different loss.

9. What Distinguishes Variational Autoencoders From Other Autoencoders?
In contrast to other forms of autoencoders, variational autoencoders are generative models. Variational autoencoders are frequently utilized in generative tasks because they, like GANs, learn the distribution of the training set.

10. What is the Difference Between an Autoencoder and PCA in Terms of Dimensionality Reduction?
The following are some ways that it varies from PCA (Principal Component Analysis):
With numerous layers and a non-linear activation function, an autoencoder can learn non-linear transformations.
Convolutional layers can be used to learn instead of thick layers, which turns out to be more effective for video, image, and series data.
Additionally, learning multiple neural network layers using an autoencoder is more effective than learning a single, massive change with PCA.
It can use transfer learning to improve the encoder/decoder by using pre-trained layers from another model.

11. What is the need for contractive autoencoders?
We employ contractive autoencoders to make sure that our encodings are more resistant to minor perturbations found in the training set.
The representations that are too sensitive to the input are penalized by the contractive autoencoders' introduction of a new penalty term in the loss function.

12. Can you use Batch Normalisation in Sparse Auto-encoders?
There is a research that suggests a novel detector using a batch normalization masked assessment model to increase the precision of the grasping detection.
It is built utilizing a two-layer sparse autoencoder, and the second layer of the model incorporates a Batch Normalization-based mask to efficiently decrease the weakly correlated features.
The more different features that are extracted from such a model ensure that the grasping detection will be more accurate.

13. Describe how the convolutional autoencoders' encoder and decoder functions.
We send the input image to the convolutional layer-based encoder.
Convolution is carried out by the convolutional layer, which also extracts significant features from the image.
Then, using the max pooling technique to keep only the most crucial aspects of the image, we produce a latent image representation known as a bottleneck.
We provide the bottleneck as an input to the decoder.
The decoder performs the deconvolution operation and attempts to rebuild the image from the bottleneck using deconvolutional layers.

14. Difference between overcomplete and undercomplete autoencoders
The autoencoder is referred to as an overcomplete autoencoder when the dimension of the code or latent representation is greater than the dimension of the input. On the other hand, the autoencoder is known as an undercomplete autoencoder when the dimension of the code or latent representation is smaller than the dimension of the input.

15. How can you evaluate the performance of an autoencoder?
Because autoencoders are data-specific, they can only compress data that is identical to the data they were trained on. The effectiveness of the method might therefore be assessed based on the usefulness of traits that have been learned via hidden layers.
This is why, in my opinion, cutting the output of the intermediate hidden layer and comparing the accuracy/performance of your chosen algorithm using this reduced data rather than the original data is a good way to assess an autoencoder's effectiveness in dimensionality reduction.

16. How are autoencoders used for image denoising?

The autoencoders for denoising images can be used. In order to provide the encoder with corrupted input rather than raw input, we first add some noise to the input to corrupt it.
The encoder will discover that the noise is undesired information as it learns the representation of the input and will delete its representation. In order to transfer the learnt representation to the bottleneck, the encoder learns a compact representation of the input that excludes noise and retains only the information that is necessary.
The decoder then reconstructs the image using the bottleneck caused by the input. The decoder can produce a denoised image from the bottleneck since there is no representation of the noise in the bottleneck.

17. Explain how autoencoders can be used for anamoly detection?
To find anomalies in a high-dimension dataset, follow these steps. This also works with unbalanced datasets.
Don't enter any unusual transactions into the encoder during the training. The latent representation of the typical input data will be taught to the bottleneck layer.
The Decoder will rebuild the typical transactions of the initial input data using the output from the bottleneck layers.
The two types of transactions will be dissimilar in a fraudulent transaction. The fraudulent transaction will be difficult for the Autoencoder to reconstruct, which will cause a significant level of reconstruction error.
On the basis of a chosen threshold value for the reconstruction error, you can flag a new transaction as fraudulent.

18. How to reverse PCA and reconstruct original variables from several principal components?
PCA calculates the covariance matrix's eigenvectors ("principal axes") and ranks them according to their eigenvalues (amount of explained variance). The principal components can then be produced by projecting the centered data onto these principal axes ("scores"). One can only maintain a portion of the principle components for dimensionality reduction and throw away the rest.

19. Why use a autoencoder for dimensionality reduction?
Take into account a feed-forward fully linked auto-encoder with input layer, 1 hidden layer with k units, 1 output layer, and all linear activation functions.
The latent space of this auto-encoder spans the first k fundamental elements of the original data. If you wish to represent the input with fewer features but aren't particularly concerned with the orthogonality restriction in PCA.

However, auto-encoders permit a variety of modifications on this fundamental concept, providing you more options than PCA for how the latent space should be built. It is obvious that using CNN layers in place of FFNs results in a different kind of model than PCA, and as a result, it will encode various kinds of information in the latent space. Another alternative to PCA's latent encoding is to use nonlinear activation functions (because PCA is linear). Sparse, contractive, and variational auto-encoders also have different objectives than PCA and will provide different outcomes, which can be useful depending on the issue you're trying to resolve.

20) What are the important hyper parameters that need to set before training autoencoder?
Before training an autoencoder, we must establish the following 4 hyperparameters:
Code size: Number of middle layer nodes is a measure of code size.
More compression is achieved with smaller dimensions.
Number of layers: We are free to choose the depth of the autoencoder.
Without taking into account the input and output, the encoder and decoder in the aforementioned diagram both have two layers.
Nodes per layer: Because the layers are stacked one on top of the other, the autoencoder design we're working on is known as a stacked autoencoder. Autoencoder stacks frequently resemble switches. Less nodes per layer in the encoder with each additional layer leads to more nodes per layer in the decoder. In terms of layer structure, the decoder and the encoder are also symmetric. Since we have complete control over these factors, as was previously mentioned, this is not essential.
Loss function: We have two options for the loss function: binary crossentropy or mean squared error (mse). Crossentropy is commonly used if the input values fall within the [0, 1] range; otherwise, mean square error is employed.

21. How to reverse max pooling layer in autoencoder to return the original shape in decoder?

input = layers.Input(shape=(28, 28, 1))

# Encoder
x = layers.Conv2D(32, (3, 3), activation="relu", padding="same")(input)
x = layers.MaxPooling2D((2, 2), padding="same")(x)
x = layers.Conv2D(32, (3, 3), activation="relu", padding="same")(x)
x = layers.MaxPooling2D((2, 2), padding="same")(x)

# Decoder
x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation="relu", padding="same")(x)
x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation="relu", padding="same")(x)
x = layers.Conv2D(1, (3, 3), activation="sigmoid", padding="same")(x)

# Autoencoder
autoencoder = Model(input, x)
autoencoder.compile(optimizer="adam", loss="binary_crossentropy")
autoencoder.summary()

A 2D tensor can be upscaled in a variety of ways, or it can be projected from a smaller vector into a bigger one.

Here's a non exhaustive list:

1. Apply a single or a few upsampling layers, then a flatten layer, then a linear layer.To expand the size of your image, upsampling essentially uses common image upscaling algorithms.It should then be flattened so that a linear layer may be added to it, giving you the precise form you need.

2. Apply a flatten, then a projection layer, skipping the upscale entirely.
   This will do for MNIST. Use the previously mentioned advice, interspersed with convolutional blocks, for more complicated datasets to boost your models' capacity and reconstruction abilities.

You have already tried the UpSampling + Conv route. Applying a flatten layer, a projection layer with 768 output units, and then reshaping into batch, 28, 28, and 1 once more will provide you the results you require.

22. Why do we need Denoising?

# Since we only need images from the dataset to encode and decode, we
# won't use the labels.
(train_data, _), (test_data, _) = mnist.load_data()

# Normalize and reshape the data
train_data = preprocess(train_data)
test_data = preprocess(test_data)

# Create a copy of the data with added noise
noisy_train_data = noise(train_data)
noisy_test_data = noise(test_data)

# Display the train data and a version of it with added noise
display(train_data, noisy_train_data)

Data is distorted in some way during the denoising process by adding random noise, and the model is trained to anticipate the original, uncorrupted data.
A different approach to this is to leave out portions of the input rather than introducing noise so that the model can learn to predict the original image.
The objective here is to save the encoder's output as a feature vector so that it may be used in a supervised model train-prediction technique.

The use of denoising autoencoders can be aimed at cleaning up old stained scanned images or help with feature selection efforts in cancer biology. Regarding, the output of an old image encoder contributes to a model's ability to recover the original image utilizing strong latent representations produced by the decoder. In terms of cancer biology, the retrieved encoder features aid in the development of a more accurate cancer diagnosis.

23. How do we add a sparsity constraint on the encoded representations

from keras import regularizers

encoding_dim = 32

input_img = keras.Input(shape=(784,))
# Add a Dense layer with a L1 activity regularizer
encoded = layers.Dense(encoding_dim, activation='relu',
                activity_regularizer=regularizers.l1(10e-5))(input_img)
decoded = layers.Dense(784, activation='sigmoid')(encoded)

autoencoder = keras.Model(input_img, decoded)

Less units would "fire" at a given moment if the activity of the hidden representations were subject to a sparsity constraint, which is added when the representations are constrained to be compact. This can be accomplished in Keras by including an activity regularizer in our Dense layer:

24.Can you explain when to use use sequence-to-sequence autoencoder?

timesteps = ...  # Length of your sequences
input_dim = ... 
latent_dim = ...

inputs = keras.Input(shape=(timesteps, input_dim))
encoded = layers.LSTM(latent_dim)(inputs)

decoded = layers.RepeatVector(timesteps)(encoded)
decoded = layers.LSTM(input_dim, return_sequences=True)(decoded)

sequence_autoencoder = keras.Model(inputs, decoded)
encoder = keras.Model(inputs, encoded)

You might wish to employ an encoder and decoder that can capture temporal structure, such as an LSTM, if your inputs are sequences rather than vectors or 2D images. In order to create an LSTM-based autoencoder, you must first use an LSTM encoder to turn your input sequences into a single vector that contains details about the entire sequence. Next, you must repeat this vector n times (where n is the number of timesteps in the output sequence), and finally, you must use an LSTM decoder to convert this constant sequence into the desired sequence.

25. Why do we use binary cross entropy loss on autoencoders?

input = layers.Input(shape=(28, 28, 1))

# Encoder
x = layers.Conv2D(32, (3, 3), activation="relu", padding="same")(input)
x = layers.MaxPooling2D((2, 2), padding="same")(x)
x = layers.Conv2D(32, (3, 3), activation="relu", padding="same")(x)
x = layers.MaxPooling2D((2, 2), padding="same")(x)

# Decoder
x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation="relu", padding="same")(x)
x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation="relu", padding="same")(x)
x = layers.Conv2D(1, (3, 3), activation="sigmoid", padding="same")(x)

# Autoencoder
autoencoder = Model(input, x)
autoencoder.compile(optimizer="adam", loss="binary_crossentropy")
autoencoder.summary()

The values of the input data have an impact on the loss function. Binary crossentropy is acceptable as the loss function if the input data are limited to values between zero and one (rather than values outside of these ranges). If not, you must use alternative loss functions like "mse" (mean squared error) or "mae" (i.e. mean absolute error). Note that you can use binary crossentropy, as it is often used, for input values in the range [0, 1]. (e.g. Keras autoencoder). Expect the loss value to remain positive, though, as binary crossentropy does not return zero when neither the prediction nor the label are either zero or one (no matter they are equal or not).