letter_digit_generator_VAE

This project aims to build a conditional variational autoencoder (CVAE) to generate arbitrary handwritten letters/digits based on the keyboard input. Based on the EMNIST dataset, the CVAE model is trained to encode the handwritten letters/digits into a latent vector space. With a random sampling or interpolation technique, imaginary letters and digits are generated.

EMNIST data examples

LDG Version 3

• Loss: binary crossentropy
• Optimizer: Adam
• Latent dimension: 6
• Image normalization: [0, 1]
• Last activation function of the decoder: sigmoid
• Convolutional CVAE layers: [784,62]-[784]-[(28,28,1)]-[(14,14,16)]-[(7,7,32)]-[1568]-[64]-[6] // [6,62]-[64]-[1568]-[(7,7,32)]-[(14,14,32)]-[(28,28,16)]-[(28,28,1)]-[784]
• Multi-layer CVAE layers: [784,62]-[256]-[128]-[6] // [6,62]-[128]-[256]-[784]

ldg_v3_gen

A command-line letters/digits generator based on the ldg_v3 Conv-CVAE model (details below). It simply loads the Conv-CVAE model and the corresponding best weights to produce results.

letter_digit_generator_convolutional-CVAE and letter_digit_generator_multi-layer-CVAE

• label inputs to both encoder and decoder

Training

Dataset reconstruction

Generating new letters/digits (with/without arbitrary binary threshold filter)

LDG Version 2

• Loss: MSE
• Optimizer: Adam
• Latent dimension: 10
• Image normalization: [-1, 1]
• Last activation function of the decoder: tanh
• Convolutional CVAE layers: [784,62]-[784]-[(28,28,1)]-[(28,28,16)]-[(28,28,32)]-[(28,28,64)]-[12544]-[128]-[10] // [10,62]-[128]-[12544]-[(14,14,64)]-[(28,28,32)]-[(28,28,16)]-[(28,28,1)]-[784]
• Multi-layer CVAE layers: [784,62]-[512]-[256]-[10] // [10,62]-[256]-[512]-[784]

ldg_v2_gen

A command-line letters/digits generator based on ldg_v2 Conv-CVAE model (details below). It simply loads the Conv-CVAE model and the corresponding best weights to produce results.

letter_digit_generator_convolutional-CVAE and letter_digit_generator_multi-layer-CVAE

• label inputs to both encoder and decoder

Training (direct comparison is difficult due to the difference in epochs)

Dataset reconstruction

Generating new letters/digits (with/without arbitrary binary threshold filter)

LDG Version 1

letter_digit_generator_convolutional-CVAE

Initial convolutional conditional variational autoencoder model.

• label inputs only to decoder
• training/test data reconstructions were satisfactory, but generation of specific string input was somewhat difficult.

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(ARCHIVE) convolutional VAE: initial model building and function testings on F_MNIST

VAE interpolation from image 1 to image 2

(ARCHIVE) Dog_crossbreed_prediction

While the model architecture seems to be okay, the standford dogs datasets may not be suitable to train VAE.