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Google engineers talk about the importance of Bengio's deep learning papers
The paper titled *"Understanding Deep Learning Needs to Rethink Generalization"* has sparked widespread discussion and raised many questions among researchers. It was even debated on platforms like Quora. Eric Jang, a Google Brain engineer, believes that understanding the mechanisms behind deep learning can significantly enhance its real-world applications. Additionally, Zhang et al. (2016) may serve as an important reference point in this ongoing conversation.
In 2017, many machine learning researchers were focused on a fundamental question: How do deep neural networks function? Why are they so effective at solving real-world problems?
Even for those who aren't deeply involved in theoretical analysis or algebra, gaining insight into how deep learning works can help us better apply it in practical scenarios. The paper *“Understanding Deep Learning Requires Rethinking Generalizationâ€* reveals some fascinating properties of neural networks. Specifically, it shows that these networks have the capacity to memorize random data completely. Under Stochastic Gradient Descent (SGD) settings, they can reduce training errors even on datasets as large as ImageNet.
This contradicts the traditional narrative that "deep learning miraculously discovers low-level, intermediate, and high-level features, similar to how the mammalian brain’s V1 system behaves during data compression."
Between 2012 and 2015, many researchers used the concept of *inductive bias* to explain how deep networks achieve low test errors, suggesting a form of generalization. However, if a deep network can memorize random data, it implies that inductive bias alone is not sufficient to explain generalization—since it can also lead to pure memorization (e.g., through architectures like convolutional layers, dropout, or batch normalization).
One reason this paper gained so much attention is that it won the “Perfect Score†and the ICLR 2017 Best Paper Award. This sparked intense discussions, creating a feedback loop of interest. I believe this is a strong paper because it raises a question that hadn’t been asked before and provides solid experimental evidence supporting intriguing findings.
However, I think it may take 1–2 years for the deep learning community to fully recognize the significance of such a paper, especially when conclusions are based more on empirical results than analytical reasoning.
Tapabrata Ghosh pointed out that some researchers argue that while deep networks can indeed memorize data, this might not be their primary behavior in practice. They suggest that the time required to "memorize" a semantically meaningful dataset is shorter than that needed for random data, implying that deep networks can exploit the underlying semantic structure in the training set.
I believe Zhang et al. (2016) could become a key indicator in understanding how deep networks operate, but it doesn't fully resolve the issue of generalization. Someone might challenge the paper’s claims soon, which is part of the nature of experimental science.
In summary, this paper is considered important because it demonstrates that deep learning can learn random databases through memory. It then raises a crucial follow-up question: How do deep networks learn non-random, structured datasets?
Here are my thoughts on the generalization problem:
A high-capacity parametric model with a good optimization objective absorbs data like a sponge. I believe the optimization goal of deep networks is somewhat "lazy" but powerful: when given the right model bias and compatible input data, deep networks can build a semantic feature hierarchy. However, if optimization becomes difficult, they may simply resort to memorizing the data.
What we currently lack is a way to control the balance between memory and generalization, and we don't yet have robust tools to effectively manage this trade-off, such as weight regularization or dropout.