#76 - Carola Schönlieb

Hybrid Models

Carola Schönlieb explores the integration of handcrafted models with neural networks in image processing. She emphasizes the interpretability and guarantees provided by handcrafted models, which are often lacking in purely neural network-based approaches. Carola explains that feeding neural networks with additional information, such as prior data, can enhance their performance and reliability 1. This hybrid approach aims to leverage the strengths of both methods for better results in tasks like computer tomography 2.

You have a handcrafted model in the end that you still understand, right. And that you can still prove things about. You still have guarantees on your solution.

--- Carola Schönlieb

Carola also discusses the current research status, where different teams are experimenting with various ways to combine these models 2.

   

Optimization

Optimization techniques are crucial for training neural networks in image processing tasks. Carola Schönlieb explains that sequential optimization, where data is processed bit by bit, is often more effective than feeding the network with all data at once. She highlights the importance of stochastic optimization, which involves randomly selecting variables to optimize in each sweep through the network 3. This method helps in generalizing the model to work well on unseen data, rather than overfitting to the training set.

The main thing in machine learning is stochastic optimization. So you don't minimize, you know exactly for all the variables that you have, but you randomly pick a certain amount in every sweep through the network that you're optimizing for.

--- Carola Schönlieb

Carola notes that this approach allows for better generalization, making the neural network more robust and versatile 3.