# Continual training on new data

def train(model, data):

    for batch in data:

        inputs, targets = batch

        loss = model.train(inputs, targets)

        # Update model parameters

        model.update_params(loss)

# User feedback-based improvement using Reinforcement Learning

def train_with_feedback(model, data, feedback):

    for batch, feedback in zip(data, feedback):

        inputs, targets = batch

        # Generate image

        image = model.generate(inputs)

        # Compute reward based on feedback

        reward = compute_reward(image, feedback)

        # Update model parameters using Reinforcement Learning

        model.update_params_reinforce(inputs, targets, reward)

# Adding a feature to select the areas of the image that need improvement and correct them

def improve_image(image):

    # Perform image segmentation to identify areas needing improvement

    segments = perform_segmentation(image)

    # Perform image inpainting to correct areas needing improvement

    new_image = perform_inpainting(image, segments)

    return new_image