18CSE484T - Deep Learning Unit 4 & 5 (4 MARKS)

 4M:


Regularized autoencoder:

  • Undercomplete autoencoders can fail to learn anything useful if the encoder and decoder are given too much capacity

  • Unlike those, Regularized autoencoders use a loss function that encourages the model to have other properties besides the ability to copy its input to its output

  • Sparsity of the representation

  • Robustness to noise or to missing inputs

  • Smallness of the derivative of the representation

  • Types of regularized autoencoder:

  • Sparse autoencoder

  • Denoising autoencoder


Compare and contrast autoencoder vs CNN

Autoencoders: 

  • Learn a compact representation of the dat for reconstruction

  • Typically consists of fully connected layers

  • It is an unsupervised learning technique

  • Applicable to general purpose tasks

CNN:

  • Extract hierarchical features from images for classification or other tasks

  • Consists of convolutional, pooling and fully connected layers

  • It is a supervised learning technique

  • Specialized for image related tasks

While both autoencoders and CNNs learn representations, autoencoders focus on data compression and reconstruction whereas CNNs specialize in extracting features from images for classification and other visual tasks


Applications of autoencoders

  • An autoencoder is a type of artificial neural network used to learn efficient dat codings in an unsupervised manner

  • Applications:

  • Image reconstruction

  • Image colorization

  • Image generation

  • Image denoising

  • Image compression

  • Anomaly detection

  • Dimensionality reduction

  • Sequence to sequence prediction

  • Recommendation system


Undercomplete autoencoder

  • An autoencoder whose code dimension is smaller than the input dimension is called under complete

  • When the encoder and decoder are linear and L is the mean squared error, an under complete autoencoder learns to span the same subspace as PCA

  • Under complete autoencoders do not need any regularization as they maximize the probability of the data rather than copying the input to the output

  • Has a smaller dimension for hidden layer compare to the input layer and this helps in obtaining important features from the data

  • Objective is to minimize the loss function by penalizing the g(f(x)) 


Dimensionality reduction by autoencoder

  • An autoencoder whose code dimension is smaller than the input dimension is called under complete

  • The size of the hidden layer is smaller than the input layer

  • We force the network to learn important features by reducing the hidden layer size

  • Dimensional reduction methods are based on the assumption that dimension of data is artificially inflated and its intrinsic dimension is much lower


Siamese networks:

  • A Siamese neural network is also called a twin neural network

  • It is an ANN which contains two or more identical subnetworks which means they have the same configuration with the same parameters and weights

  • Usually, we only train one of the subnetworks and use the same configuration for other sub-networks

  • These networks are used to find the similarity of the inputs by comparing their feature vectors

  • We will have two encodings F(A) and F(B) and we will compare them to know how similar they are


RCNN - Object Detection

  • Region Based Convolutional Neural Network uses a Selective Search algorithm to detect possible locations of an object in an image

Steps followed in RCNN to detect objects:

  • Take a pre-trained CNN

  • Then this model is restrained. We train the last layer of the neural network based on the number of classes that need to be detected

  • Next is to get the Region of Interest for each image and reshape the region to match with CNN input size

  • After getting the regions, we train the SVM to classify the objects and the background

  • Finally we train a linear regression model to generate tighter bounding boxes for each identified object in the image


3D CNN – Event detection

  • 3D-CNNs are an extension of traditional 2D-CNNs specifically designed for video and spatiotemporal data

  • Key features:

  • Temporal axis

  • 3D filters

  • Integration of motion information

  • Feature extraction and classification

  • Video event detection:

  • 3D-CNNs are used to extract spatial features from video frames

  • Subsequent layers predict events and their locations

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