A Beginner's Guide to Upsampling with Convolutional Neural Networks

 

As a beginner in the field of deep learning, it's important to understand the concept of upsampling and how it can be implemented using Convolutional Neural Networks (CNNs). Upsampling is a technique used to increase the resolution of an image or feature map, which is often required in applications such as image super-resolution, style transfer, and image-to-image translation.

 

In this article, we will discuss the need for upsampling in Generative Adversarial Networks (GANs), two common methods of upsampling with CNNs - Upsampling Layer and Transpose Convolutional Layer, and some further reading for those interested in exploring the topic in more depth.

Need for Upsampling in GANs

GANs are a popular type of deep learning model used for image generation. They consist of two neural networks - a generator and a discriminator - that work together to generate realistic images. The generator creates new images that are then evaluated by the discriminator. The generator is trained to create images that fool the discriminator, and the discriminator is trained to correctly identify whether an image is real or generated.

To create high-quality images, the generator needs to increase the resolution of the image it generates. This is where upsampling comes in. Upsampling allows the generator to create higher resolution images by increasing the number of pixels in the image.

How to Use the Upsampling Layer

One method of upsampling with CNNs is using an upsampling layer. The upsampling layer is a simple layer that increases the resolution of the input feature map by a factor of two. It works by inserting zeros between each pixel in the input feature map and then applying a convolutional filter to the resulting feature map.

The following code demonstrates how to use the upsampling layer in Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import UpSampling2D,Conv2D

model = Sequential([
    Conv2D(64, (3,3), padding='same', activation='relu', input_shape=(64,64,3)),
UpSampling2D()
])
# summarize model
model.summary()

 

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d_1 (Conv2D)           (None, 64, 64, 64)        1792      
                                                                 
 up_sampling2d_1 (UpSampling  (None, 128, 128, 64)     0         
 2D)                                                             
                                                                 
=================================================================
Total params: 1,792
Trainable params: 1,792
Non-trainable params: 0
_________________________________________________________________

In this example, we add an upsampling layer after a convolutional layer with 64 filters, a 3x3 kernel, and 'same' padding. The input shape is (64, 64, 3), which represents an RGB image of size 64x64.

How to Use the Transpose Convolutional Layer

Another method of upsampling with CNNs is using a transpose convolutional layer. This layer is also known as a deconvolutional layer, and it works by learning a filter that increases the resolution of the input feature map.

The following code demonstrates how to use the transpose convolutional layer in Keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2DTranspose

model = Sequential([
    Conv2DTranspose(64, (3,3), strides=(2,2),padding='same', activation='relu', input_shape=(64,64,3))
])
model.summary()

 

Model: "sequential_3"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d_transpose_1 (Conv2DT  (None, 128, 128, 64)     1792      
 ranspose)                                                       
                                                                 
=================================================================
Total params: 1,792
Trainable params: 1,792
Non-trainable params: 0
_________________________________________________________________

In this example, we add a transpose convolutional layer after a convolutional layer with 64 filters, a 3x3 kernel, and 'same' padding. The input shape is (64, 64, 3), which represents an RGB image of size 64x64. The strides argument is set to (2,2), which means the output feature map will have twice the resolution of the input feature map.

Further Reading

For those interested in learning more about upsampling with CNNs, here are some recommended resources:

• UpSampling2D layer documentation in Keras - official documentation for the Upsampling2D layer in Keras.

• Conv2DTranspose layer documentation in Keras - official documentation for the Conv2DTranspose layer in Keras.

• Image-to-Image Translation with Conditional Adversarial Networks - a research paper that uses upsampling with CNNs for image-to-image translation.

• Super-Resolution using Deep Convolutional Networks - a research paper that uses upsampling with CNNs for image super-resolution.

Summary

In this article, we discussed the concept of upsampling and how it can be implemented using Convolutional Neural Networks (CNNs). We explained the need for upsampling in Generative Adversarial Networks (GANs), and two common methods of upsampling with CNNs - Upsampling Layer and Transpose Convolutional Layer. We also provided some further reading for those interested in exploring the topic in more depth.

By using upsampling with CNNs, we can increase the resolution of images and feature maps, which is useful in applications such as image super-resolution, style transfer, and image-to-image translation. As a beginner in deep learning, understanding the concept of upsampling and its implementation with CNNs is an important step towards building more advanced models.