Neural networks are really good at memorizing random labels. And yet, even when they do so, they can still generalize pretty well. Additionally, common regularization methods aren't even neccessary for this generalization. This goes against standard bias-variance tradeoff and overfitting narratives, indicating we don't have a great understanding of how and why neural networks perform and generalize so well.

The authors demonstrated that standard deep learning models can easily fit random labels. This is counter-intuitive because it suggests that these models have enough capacity to memorize even completely random data. This goes against the traditional understanding of overfitting, where a model with high capacity might overfit to the training data and perform poorly on unseen data.

They also showed that explicit regularization methods {like weight decay, dropout, data augmentation, etc.} are not necessary for these models to generalize well, again contradicting conventional wisdom. While these regularization methods can improve model performance, the models still generalized well without them.

In addition, they observed that deep learning models can fit the training data perfectly, achieving zero training error, but still perform well on the test data. This goes against the bias-variance trade-off concept, which posits that a model that fits the training data too well {i.e., a model with high variance} would perform poorly on unseen data.

To demonstrate these points, the authors conducted a series of experiments with deep learning models trained on the CIFAR-10 dataset. In one set of experiments, they replaced the true labels with random labels and showed that the models could fit these random labels perfectly. In another set of experiments, they trained the models without any regularization and found that they still generalized well.

The authors argued that these observations suggest that the traditional statistical learning theory does not fully explain why deep learning models generalize well. They proposed that other factors, such as the optimization algorithm and the structure of the model architecture, might play important roles in the generalization of deep learning models. For example, the stochastic gradient descent {SGD} optimization algorithm, which is commonly used to train deep learning models, has an implicit regularization effect.

However, the authors did not provide a definitive explanation for their observations. They suggested that understanding deep learning requires rethinking generalization and proposed that more research is needed to develop new theories and frameworks that can explain the generalization behavior of deep learning models.

In conclusion, the paper challenged the conventional understanding of overfitting and generalization in deep learning and suggested that new theories are needed to explain why these models generalize well. The work has stimulated a lot of subsequent research into the theory of deep learning, aiming to bridge the gap between the empirical success of deep learning and our theoretical understanding of it.