Summary of Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies?, by Jan Rathjens and Laurenz Wiskott
Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies?
by Jan Rathjens, Laurenz Wiskott
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Predictive coding-inspired deep networks for visual computing integrate classification and reconstruction processes in shared intermediate layers. While synergy between these processes is commonly assumed, it has yet to be convincingly demonstrated. By utilizing a purposefully designed family of model architectures reminiscent of autoencoders, each equipped with an encoder, a decoder, and a classification head featuring varying modules and complexities, our approach meticulously analyzes the extent to which classification- and reconstruction-driven information can seamlessly coexist within the shared latent layer of the model architectures. Our findings underscore a significant challenge: Classification-driven information diminishes reconstruction-driven information in intermediate layers’ shared representations and vice versa. While expanding the shared representation’s dimensions or increasing the network’s complexity can alleviate this trade-off effect, our results challenge prevailing assumptions in predictive coding and offer guidance for future iterations of predictive coding concepts in deep networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive coding-inspired deep networks are special computers that help us understand what we see. These networks do two things: they figure out what something is (like a cat or dog), and they reconstruct what it looks like. But until now, nobody has shown how these two processes work together in the same computer. We designed a family of models to test this. Each model had three parts: one that looked at the data, one that made predictions, and another that reconstructed what it saw. Our results show that when we tell the computer what something is, it makes it harder for the computer to reconstruct what it looks like. But if we make the computer’s brain bigger or more complicated, this problem goes away. This challenges our understanding of how these computers work, but it also gives us ideas for making them better. |
Keywords
* Artificial intelligence * Classification * Decoder * Encoder
