Reading Assignment: Advanced CNN Architectures

Main Reading

Start with the high-level overview provided in this blog post by Adit Deshpande. Anything beyond the section on region-based CNNs is optional. (GANs are covered in the “Learning Generative Models” course.)

Next, read Densely connected convolutional networks (2016), Huang et al. There is a lot to learn from this paper as the authors do a very good job pointing out similarities and differences with many related approaches.

Selective Reading (i.e. pick one!)

As additional (mandatory) reading assignment, here are 6 papers that use (and extend) CNNs in various settings. They are all worth reading, but you just need to read one of them.

Please use the poll provided on Mattermost to choose your paper by voting! Pick the one you are most interested in unless it already has 7 votes. In that case, consider reading a less popular one because we are aiming for a balanced distribution. Of course, you can also read more than one paper.

Here are some questions to guide you during reading:

• What is the learning problem? (datasets, representation of inputs and outputs, cost function and evaluation measures) Which specific challenges are addressed?
• What does the network design look like? (Try to understand the details as if you were to implement the described architecture!)
• What are the main innovations described in the paper? Where does it go beyond the techniques covered in the deep learning book?
• What are the main experimental findings?
• Does the paper have any weak spots?
• Is there any information on performance (throughput) and hardware requirements?

You should be prepared to explain the most important / innovative aspects of the paper to others.

1. End-to-end Learning for Music Audio Tagging at Scale (2018), J. Pons et al.

2. Xception: Deep Learning with Depthwise Separable Convolutions (2016), F. Chollet

3. U-Net: Convolutional Networks for Biomedical Image Segmentation (2015), O. Ronneberger et al.

4. Language Modeling with Gated Convolutional Networks (2016), Y. N. Dauphin et al.

5. You only look once: Unified, real-time object detection (2016), J. Redmon et al.

6. Harmonic Convolutional Networks based on Discrete Cosine Transform (2021), M. Ulicny et al.

Optional Further Reading

If you would like to dig deeper, here are some more ressources:

• on ResNets: Deep residual learning for image recognition (2016), K. He et al.
• on Inception architectures: Rethinking the inception architecture for computer vision (2016), C. Szegedy et al.
• on 1x1 convolution: One by One [ 1 x 1 ] Convolution - counter-intuitively useful (2016), A. Prakash
• on convolution arithmatic A guide to convolution arithmetic for deep learning (2016), V. Dumoulin & F. Visin