Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
5 (2)
Book Image

Modern Computer Vision with PyTorch

5 (2)
By: V Kishore Ayyadevara, Yeshwanth Reddy

Overview of this book

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.
Table of Contents (25 chapters)
Section 1 - Fundamentals of Deep Learning for Computer Vision
Section 2 - Object Classification and Detection
Section 3 - Image Manipulation
Section 4 - Combining Computer Vision with Other Techniques

Chapter 13 - Advanced GANs to Manipulate Images

  1. Why do we need a Pix2Pix GAN where a supervised learning algorithm like U-Net could have worked to generate images from contours?
    U-net only uses pixel-level loss during training. We needed pix2pix since there is no loss for realism when a U-net generates images.
  2. Why do we need to optimize for 3 different loss functions in CycleGAN?
    Answer provided in the 7 points in CycleGAN section.
  3. How do the tricks leverage in ProgressiveGAN help in building a StyleGAN?
    ProgressiveGAN helps the network to learn a few upsampling layers at a time so that when the image has to be increased in size, the networks responsible for generating current size images are optimal.
  4. How do we identify latent vectors corresponding to a given custom image?
    By adjusting the randomly generated noise in such a way that the MSE loss between the generated image and the image of interest is as minimal as possible.