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S.C.O.R.P

Sentence Conditioned Object Rendering Program - Text-To-Image GAN Model - AI


MNIST Dataset - Research Paper

The following paper below is based on the results of training S.C.O.R.P on the MNIST dataset.

Abstract

This research paper introduces S.C.O.R.P, a Generative Adversarial Network (GAN) designed to generate numerical digits between 0 and 9. The primary objective of S.C.O.R.P is to produce synthetic digits, which can be used in various applications such as data augmentation, handwritten digit recognition, and generative art. In this paper, I present the architecture, training process, and evaluation results of S.C.O.R.P.

Introduction

Generative Adversarial Networks (GANs) have gained significant popularity for their ability to generate images. S.C.O.R.P is a specialized GAN model tailored for generating numerical digits due to being trained off of the MNIST dataset for this paper. The generation of accurate and diverse digit images is essential for many computer vision and machine learning tasks. S.C.O.R.P leverages a novel architecture by using GAN, instead of using newfound methods like diffusion, and training strategy to achieve digit generation.

Architecture

S.C.O.R.P consists of two main components: a generator and a discriminator.

Generator

The generator is responsible for producing synthetic digit images. It utilizes a deep convolutional neural network (CNN) architecture to generate images from random noise. The generator starts with a latent vector of random noise and progressively upsamples it through a series of convolutional layers. Batch normalization and ReLU activation functions are used to stabilize training and introduce non-linearity. The final output is a 28x28 grayscale image representing the generated digit.

Discriminator

The discriminator is responsible for distinguishing between real and fake digit images. It also employs a CNN architecture, which takes input images and outputs a probability score indicating the likelihood of an image being real or fake. The discriminator is trained to maximize its ability to discriminate while the generator is trained to minimize the discriminator's ability to differentiate between real and fake images.

Training

S.C.O.R.P is trained using a dataset of real handwritten digit images. The loss function for the generator and discriminator is a binary cross-entropy loss. The training process involves alternating between updating the generator and the discriminator in a process called adversarial training. The generator's goal is to produce digit images, while the discriminator's goal is to become more proficient at distinguishing real from fake images. This adversarial process continues until a convergence criterion is met. The image resolution goes up the longer it trains.

Evaluation

The performance of S.C.O.R.P is evaluated using several metrics, including:

  1. Fidelity: I measure how closely the generated digits resemble real digits using metrics like the Structural Similarity Index (SSI) and Mean Squared Error (MSE).

  2. Diversity: I assess the diversity of generated digits by evaluating the distribution of generated digit classes.

  3. Robustness: S.C.O.R.P is tested against various perturbations and noise to evaluate its robustness.

  4. Realism: Human evaluators are asked to rate the realism of generated digits in a subjective evaluation.

Results

S.C.O.R.P. demonstrates results in terms of fidelity, diversity, and robustness. The generated digits closely resemble real hand-drawn digits and exhibit a wide range of variations. The model is robust to noise and perturbations, making it suitable for real-world applications depending on how long it trains.

Conclusion

S.C.O.R.P.'s ability to produce diverse digits has potential applications in various fields, including computer vision and machine learning. Future work could involve extending the model to generate multi-digit sequences or exploring other image-generation tasks.

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Text-To-Image GAN Model

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