| Oct 28, 2023 | Biraj sarmah |
The Inner Workings of Neural Network |7|
You might wonder why I initially introduced this network with the goal of capturing edges and patterns. In reality, it doesn’t align with those intentions at all. This isn’t the ultimate objective but rather a foundational starting point.
Truth be told, this technology is somewhat dated, akin to research conducted in the 1980s and 1990s. Nonetheless, comprehending it is a prerequisite for grasping more advanced contemporary variations, and it undeniably demonstrates the capability to tackle intriguing problems. However, the deeper you delve into what these hidden layers truly accomplish, the less intelligent it appears.
Shifting the focus momentarily from how networks learn to how you learn – this only happens if you actively engage with the material here. One relatively simple task I encourage you to undertake is to pause for a moment and contemplate potential modifications you’d implement to enhance the system’s ability to discern edges and patterns in images.
Even better, to actively engage with the material, I highly recommend Michael Nielsen’s book on Deep Learning and Neural Networks. In this resource, you can access the code and data for this specific example and receive a step-by-step walkthrough of the code’s operation.
What’s particularly fantastic is that this book is freely available to the public. If you find value in it, consider joining me in making a donation to support Nielsen’s contributions. I’ve also included links to a couple of other resources I find highly valuable in the description, including a remarkable blog post by Chris Olah and articles in Distil.
To wrap things up for the final few minutes, I’d like to write a snippet from a previous blog post where we discusses two recent papers that delve into how some of the more contemporary image recognition networks actually learn.
Just to pick up from where we left off in our conversation, the first paper took one of these incredibly deep neural networks that excel at image recognition. Instead of training it on a properly labeled dataset, they decided to shuffle all the labels around before the training process. Naturally, the testing accuracy would be no better than random, given that everything was essentially randomly labeled.
However, here’s the kicker – it still managed to achieve the same level of training accuracy as it would on a properly labeled dataset. This essentially implies that the millions of weights in this network were sufficient for it to just memorize the random data. This raises an intriguing question – does minimizing this cost function truly correspond to any form of structure in the image, or is it merely about memorizing the dataset’s correct classifications?
Fast forward a few months to ICML this year, there wasn’t exactly a rebuttal paper, but rather a paper that addressed some aspects of this. It revealed that these networks are actually doing something more intelligent than mere memorization. When you examine the accuracy curve, training on a random dataset resulted in a gradual decrease, almost linear in fashion. This means you struggle to discover the local minima that lead to the right weights for that accuracy.
Conversely, when training on a structured dataset with correct labels, you initially go through some adjustments, but then rapidly descend to reach the desired accuracy level. In a sense, it’s easier to find that local maximum when the data has structure.
This also brings to light another paper from a couple of years ago. While it contains many simplifications regarding the network layers, one of its significant findings was that if you scrutinize the optimization landscape, the local minima that these networks typically learn are of equal quality. In other words, if your dataset is structured you should be able to find that much more easily.
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