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:

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: