Gradient Origin Networks (Paper Explained w/ Live Coding)

Yannic Kilcher · Beginner ·📄 Research Papers Explained ·5y ago
Neural networks for implicit representations, such as SIRENs, have been very successful at modeling natural signals. However, in the classical approach, each data point requires its own neural network to be fit. This paper extends implicit representations to an entire dataset by introducing latent vectors of data points to SIRENs. Interestingly, the paper shows that such latent vectors can be obtained without the need for an explicit encoder, by simply looking at the negative gradient of the zero-vector through the representation function. OUTLINE: 0:00 - Intro & Overview 2:10 - Implicit Gene…
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Chapters (9)

Intro & Overview
2:10 Implicit Generative Models
5:30 Implicitly Represent a Dataset
11:00 Gradient Origin Networks
23:55 Relation to Gradient Descent
28:05 Messing with their Code
37:40 Implicit Encoders
38:50 Using GONs as classifiers
40:55 Experiments & Conclusion

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