Style transfer concept in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the content image for style transfer.

Computer Vision

content_image = load_image('[1]')

Drag options to blanks, or click blank then click option'

A'content.jpg'

B'style.jpg'

C'output.jpg'

D'noise.jpg'

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing the style image filename instead of the content image.

Using an output filename before processing.

The content image is the main image whose content we want to keep. We load it using the filename 'content.jpg'.

2fill in blank

medium

Complete the code to extract features from the style image using a pre-trained model.

Computer Vision

style_features = model.[1](style_image)

Drag options to blanks, or click blank then click option'

Apredict

Btransform

Cfit

Dextract_features

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' which is for classification.

Using 'fit' which is for training the model.

To get style features, we use the method 'extract_features' which returns the needed representations from the style image.

3fill in blank

hard

Fix the error in the loss calculation for style transfer.

Computer Vision

style_loss = tf.reduce_mean(tf.square([1] - target_style))

Drag options to blanks, or click blank then click option'

Acontent_features

Boutput_image

Cstyle_features

Dinput_image

Attempts:

3 left

💡 Hint

Common Mistakes

Using content features instead of style features.

Using the input or output image directly instead of features.

The style loss compares the style features of the generated image to the target style features, so we subtract 'style_features' from 'target_style'.

4fill in blank

hard

Fill both blanks to create a dictionary of style and content weights for the loss function.

Computer Vision

loss_weights = {'style': [1], 'content': [2]

Drag options to blanks, or click blank then click option'

A1e-2

B1e-4

C1e-3

D1e-5

Attempts:

3 left

💡 Hint

Common Mistakes

Setting content weight higher than style weight.

Using very small weights that make training ineffective.

Common practice is to set style weight higher (1e-2) and content weight lower (1e-3) to balance the loss.

5fill in blank

hard

Fill all three blanks to update the generated image using gradient descent.

Computer Vision

with tf.GradientTape() as tape:
    outputs = model([1])
    loss = compute_loss(outputs, [2], [3])
grads = tape.gradient(loss, [1])
optimizer.apply_gradients([(grads, [1])])

Drag options to blanks, or click blank then click option'

Agenerated_image

Bstyle_targets

Ccontent_targets

Dinput_image

Attempts:

3 left

💡 Hint

Common Mistakes

Using input image instead of generated image for updates.

Mixing up style and content targets in loss computation.

The generated image is updated by computing gradients of the loss, which compares outputs to style and content targets.