Cheat Sheet of Autoencoder and Variational Autoencoder for OSINT

Autoencoders are a type of neural network that can be used for dimensionality reduction, anomaly detection, and generative modeling. Variational autoencoders (VAEs) are a specific type of autoencoder that use a probabilistic approach to learn compressive representations.

Autoencoder

Autoencoder: A neural network that consists of an encoder and a decoder. The encoder maps the input data to a lower-dimensional representation, which is then passed through the decoder to produce the original input.
Reconstruction Error: The difference between the original input and the reconstructed output. Minimizing this error is the primary objective of autoencoder training.
Activation Functions: Used in the encoder and decoder to introduce non-linearity. Common choices include ReLU, sigmoid, and tanh.

Variational Autoencoder (VAE)

Probabilistic Modeling: VAEs use a probabilistic approach to learn compressive representations. The encoder outputs a mean and variance for the latent space.
Latent Space: A lower-dimensional representation of the input data, which captures the underlying patterns and structures.
ELBO (Evidence Lower Bound): An objective function used to train VAEs. It balances the reconstruction error with a regularization term that encourages the model to learn a meaningful latent space.

Applications in OSINT

Text Classification: Autoencoders can be used for text classification tasks, such as spam detection or sentiment analysis. VAEs can be used to learn a compressed representation of the input text.
Image Compression: Autoencoders can be used for image compression by learning a compact representation of the images.
Dataset Augmentation: VAEs can be used for dataset augmentation by generating new, synthetic data that is similar to the existing data.

Implementation in Python

Autoencoders and VAEs can be implemented using popular deep learning frameworks such as Keras or TensorFlow. The following code snippet demonstrates a simple autoencoder implementation in Python:


import numpy as np
from keras.layers import Input, Dense

class Autoencoder:
    def __init__(self, input_dim, latent_dim):
        self.input_dim = input_dim
        self.latent_dim = latent_dim
        self.encoder = self._build_encoder()
        self.decoder = self._build_decoder()

    def _build_encoder(self):
        encoder = Input(shape=(self.input_dim,))
        x = Dense(64, activation='relu')(encoder)
        x = Dense(self.latent_dim)(x)
        return encoder, x

    def _build_decoder(self):
        decoder = Input(shape=(self.latent_dim,))
        x = Dense(64, activation='relu')(decoder)
        x = Dense(self.input_dim)(x)
        return decoder, x
    
    def fit(self, X_train, epochs=100):
        self.encoder.trainable = False
        self.decoder.trainable = True
        
        for epoch in range(epochs):
            with tf.GradientTape() as tape:
                output = self.decoder(self.encoder(X_train))
                loss = metrics.mean_squared_error(X_train, output)
            gradients = tape.gradient(loss, self.decoder.trainable_variables)
            optimizer.apply_gradients(zip(gradients, self.decoder.trainable_variables))

    def call(self, input_data):
        encoder_output, x = self.encoder(input_data)
        return self.decoder(x)

# Example usage
input_dim = 784
latent_dim = 128

autoencoder = Autoencoder(input_dim, latent_dim)
X_train = np.random.rand(100, input_dim) # Replace with your own data

autoencoder.fit(X_train, epochs=10)