1 Introduction
There is no shortage of literature on what regularizers to use when training deep neural networks and how they affect the loss landscape but, to the best of our knowledge, no work has addressed when
to apply regularization. We test the hypothesis that applying regularization at different epochs of training can yield different outcomes. Our curiosity stems from recent observations suggesting that the early epochs of training are decisive of the outcome of learning with a deep neural network
achille2017critical .We find that regularization via weight decay or data augmentation has the same effect on generalization when applied only
during the initial epochs of training. Conversely, if regularization is applied only in the latter phase of convergence, it has little effect on the final solution, whose generalization is as bad as if regularization never happened. This suggests that, contrary to classical models, the mechanism by which regularization affects generalization in deep networks is not by changing the landscape of critical points at convergence, but by influencing the early transient of learning. This is unlike convex optimization (linear regression, support vector machines) where the transient is irrelevant.
In short, what matters for training deep networks is not just whether or how, but when to regularize.
In particular, the effect of temporary regularization on the final performance is maximal during an initial “critical period.” This mimics other phenomena affecting the learning process which, albeit temporary, can permanently affect the final outcome if applied at the right time, as observed in a variety of learning systems, from artificial deep neural networks to biological ones. We use the methodology of achille2017critical to regress the most critical epochs for various architectures and datasets.
Specifically, our findings are:

Applying weight decay or data augmentation beyond the initial transient of training does not improve generalization (Figure 1, Left). The transient is decisive of asymptotic performance.

Applying regularization only during the final phases of convergence does not improve, and in some cases degrades generalization. Hence, regularization in deep networks does not work by reshaping the loss function at convergence (
Figure 1, Center). 
Applying regularization only during a short sliding window shows that its effect is most pronounced during a critical period of few epochs (Figure 1
, Right). Hence, the analysis of regularization in Deep Learning should focus on the transient, rather than asymptotics.
The explanation for these phenomena is not as simple as the solution being stuck in some local minimum: When turning regularization on or off after the critical period, the value of the weights changes, so the solution moves in the loss landscape. However, test accuracy, hence generalization, does not change. Adding regularization after the critical period does change the loss function, and also changes the final solution, but not for the better. Thus, the role of regularization is not to bias the final solution towards critical points with better generalization. Instead, it is to bias the initial transient towards regions of the loss landscape that contains multiple equivalent solutions with good generalization properties.
2 Related Work
There is a considerable volume of work addressing regularization in deep networks, too vast to review here. Most of the efforts are towards analyzing the geometry and topology of the loss landscape at convergence. Work relating the local curvature of the loss around the point of convergence to regularization (“flat minima” hochreiter1997flat ; keskar2016large ; dinh2017sharp ; chaudhari2016entropy ) has been especially influential choromanska2015loss ; li2018visualizing . Other work addresses the topological characteristics of the point of convergence (minima vs. saddles dauphin2014identifying ). (jastrzkebski2017three, ; NIPS2017_6770, )
discuss the effects of the learning rate and batch size on stochastic gradient descent (SGD) dynamics and generalization. At the other end of the spectrum, there is complementary work addressing initialization of deep networks,
(glorot2010understanding, ; henaff16, ). There is limited work addressing the timing of regularization, other than for the scheduling of learning rates (smith2017cyclical, ; loshchilov2016sgdr, ).Changing the regularizer during training is common practice in many fields, and can be done in a variety of ways, either prescheduled – as in homotopy continuation methods mobahi2015theoretical , or in a manner that depends on the state of learning – as in adaptive regularization hong2017adaptive . For example, in variational stereoview reconstruction, regularization of the reconstruction loss is typically varied during the optimization, starting with high regularization and, ideally, ending with no regularization. This is quite unlike the case of Deep Learning: Stereo is illposed, as the object of inference (the disparity field) is infinitedimensional and not smooth due to occluding boundaries. So, ideally one would not
want to impose regularization, except for wading through the myriad of local mimima due to local selfsimilarity in images. Imposing regularization all along, however, causes oversmoothing, whereas the groundtruth disparity field is typically discontinuous. So, regularization is introduced initially and then removed to capture fine details. In other words, the ideal loss is not regularized, and regularization is introduced artificially to improve transient performance. In the case of machine learning, regularization is often interpreted as a prior on the solution. Thus, regularization is part of the problem formulation, rather than the mechanics of its solution.
Also related to our work, there have been attempts to interpret the mechanisms of action of certain regularization methods, such as weight decay zhang2018three ; van2017l2 ; loshchilov2017fixing ; hoffer2018norm ; krogh1992simple ; bos1996using , data augmentation vapnik2000vicinal , dropout srivastava2014dropout . It has been pointed out in (zhang2018three, ) that the GaussNewton norm correlates with generalization, and with the Fisher Information Matrix (fisher1925theory, ; amari1998natural, ), a measure of the flatness of the minimum, to conclude that the Fisher Information at convergence correlates with generalization. However, there is no causal link proven. In fact, we suggest this correlation may be an epiphenomenon: Weight decay causes an increase in Fisher information during the transient, which is responsible for generalization (Figure 5), whereas the asymptotic value of the Fisher norm (i.e., sharpness of the minimum) is not causative. In particular, we show that increasing Fisher Information can actually improve generalization.
Sensitivity (change in the final accuracy relative to unregularized training) as a function of the onset of a 50epoch regularization window. Initial learning epochs are more sensitive to weight decay compared to the intermediate training epochs for data augmentation. The shape of the sensitivity curve depends on the regularization scheme as well as the network architecture. The error bars indicate thrice the standard deviation across 5 independent trials. For experiments with weight decay (or data augmentation), we apply data augmentation (or weight decay) throughout the training.
3 Preliminaries and notation
Given an observed input (e.g.
, an image) and a random variable
we are trying to infer (e.g., a discrete label), we denote with the output distribution of a deep network parameterized by weights . For discrete , we usually have for some parametric function . Given a dataset , the crossentropy loss of the network on the dataset is defined as .When minimizing with stochastic gradient descent (SGD), we update the weights
with an estimate of the gradient computed from a small number of samples (minibatch). That is,
where is a random subset of indices of size (minibatch size). In our implementation, weight decay (WD) is equivalent to imposing a penalty to the norm of the weights, so that we minimize the regularized loss .Data augmentation (DA) expands the training set by choosing a set of random transformations of the data, (e.g., random translations, rotations, reflections of the domain and affine transformations of the range of the images), sampled from a known distribution , to yield .
In our experiments, we choose to be random cropping and horizontal flipping (reflections) of the images; are the CIFAR10 and CIFAR100 datasets (krizhevsky2009learning, ), and the class of functions are ResNet18 (he2016deep, ) and AllCNN (springenberg2014striving, ). For all experiments, unless otherwise noted, we train with SGD with momentum 0.9 and exponentially decaying learning rate with factor per epoch, starting from learning rate (see also Appendix A).
4 Experiments
To test the hypothesis that regularization can have different effects when applied at different epochs of training, we perform three kinds of experiments. In the first, we apply regularization up to a certain point, and then switch off the regularizer. In the second, we initially forgo regularization, and switch it on only after a certain number of epochs. In the third, we apply regularization for a short window during the training process. We describe these three experiments in order, before discussing the effect of batch normalization, and analyzing changes in the loss landscape during training using local curvature (Fisher Information).
Regularization interrupted.
We train standard DNN architectures (ResNet18/AllCNN on CIFAR10) using weight decay (WD) during the first epochs, then continue without WD. Similarly, we augment the dataset (DA) up to epochs, past which we revert to the original training set. We train both the architectures for 200 epochs. In all cases, the training loss converges to essentially zero for all values of . We then examine the final test accuracy as a function of (Figure 1, Left). We observe that applying regularization beyond the initial transient (around 100 epochs) produces no measurable improvement in generalization (test accuracy). In Figure 3 (Left), we observe similar results for a different data distribution (CIFAR100). Surprisingly, limiting regularization to the initial learning epochs yields final test accuracy that is as good as that achieved by regularizing to the end, even if the final loss landscapes, and hence the minima encountered at convergence, are different.
It is tempting to ascribe the imperviousness to regularization in the latter epochs of training (Figure 1, Left) to the optimization being stuck in a local minimum. After all, the decreased learning rate, or the shape of the loss around the minimum, could prevent the solution from moving. However, Figure 2 (A, curves 75/100) shows that the norm of the weights changes significantly after switching off the regularizer: the optimization is not stuck. The point of convergence does change, just not in a way that improves test accuracy.
The fact that applying regularization only at the very beginning yields comparable results, suggests that regularization matters not because it alters the shape of the loss function at convergence, reducing convergence to spurious minimizers, but rather because it “directs” the initial phase of training towards regions with multiple extrema with similar generalization properties. Once the network enters such a region, removing regularization causes the solution to move to different extrema, with no appreciable change in test accuracy.
Regularization delayed.
In this experiment, we switch on regularization starting at some epoch , and continue training to convergence. We train the DNNs for 200 epochs, except when regularization is applied late (from epoch 150/175), where we allow the training to continue for an additional 50 epochs to ensure the network’s convergence. Figure 1 (Center) displays the final accuracy as a function of the onset , which shows that there is a “critical period” to perform regularization (around epoch 50), beyond which adding a regularizer yields no benefit.
Absence of regularization can be thought of as a form of learning deficit. The permanent effect of temporary deficits during the early phases of learning has been documented across different tasks and systems, both biological and artificial achille2017critical . Critical periods
thus appear to be fundamental phenomena, not just quirks of biology or the choice of the dataset, architecture, learning rate, or other hyperparameters in deep networks.
In Figure 1 (Top Center), we see that delaying WD by 50 epochs causes a 40% increase in test error, from 5% regularizing all along, to 7% with onset epochs. This is despite the two optimization problems sharing the same loss landscape at convergence. This reinforces the intuition that WD does not improve generalization by modifying the loss function, lest Figure 1 (Center) would show an increase in test accuracy after the onset of regularization.
Here, too, we see that the optimization is not stuck in a local minimum: Figure 2 (B) shows the weights changing even after late onset of regularization. Unlike the previous case, in the absence of regularization, the network enters prematurely into regions with multiple suboptimal local extrema, seen in the flat part of the curve in Figure 1 (Center).
Note that the magnitude of critical period effects depends on the kind of regularization. Figure 1 (Center) shows that WD exhibits more significant critical period behavior than DA. At convergence, data augmentation is more effective than weight decay. In Figure 3 (Center), we observe critical periods for DNNs trained on CIFAR100, suggesting that they are independent of the data distribution.
Sliding Window Regularization.
In an effort to regress which phase of learning is most impacted by regularization, we compute the maximum sensitivity against a sliding window of 50 epochs during which WD and DA are applied (Figure 1 Right). The early epochs are the most sensitive, and regularizing for a short 50 epochs yields generalization that is almost as if we had regularized all along. This captures the critical period for regularization. Note that the shape of the sensitivity to critical periods depends on the type of regularization: Data augmentation has essentially the same effect throughout training, whereas weight decay impacts critically only the initial epochs. Similar to the previous experiments, we train the networks for 200 epochs except, when the window onsets late (epoch 125/150/175), where we train for 50 additional epochs after the termination of the regularization window which ensures that the network converges.
Reshaping the loss landscape.
regularization is classically understood as trading classification loss against the norm of the parameters (weights), which is a simple proxy for model complexity. The effects of such a tradeoff on generalization are established in classical models such as linear regression or supportvector machines. However, DNNs need not trade classification accuracy for the norm of the weights, as evident from the fact that the training error can always reach zero regardless of the amount of regularization. Current explanations (goodfellow2016deep, ) are based on asymptotic convergence properties, that is, on the effect of regularization on the loss landscape and the minima to which the optimization converges. In fact, for learning algorithm that reduces to a convex problem, this is the only possible effect. However, Figure 1 shows that for DNNs, the critical role of regularization is to change the dynamics of the initial transient, which biases the model towards regions with good generalization. This can be seen in Figure 1 (Left), where despite halting regularization after 100 epochs, thus letting the model converge in the unregularized loss landscape, the network achieves around 5% test error. Also in Figure 1 (Top Center), despite applying regularization after 50 epochs, thus converging in the regularized loss landscape, the DNN generalizes poorly (around 7% error). Thus, while there is reshaping of the loss landscape at convergence, this is not the mechanism by which deep networks achieve generalization. It is commonly believed that a smaller norm of the weights at convergence implies better generalization (wilson2017marginal, ; neyshabur2017pac, ). Our experiments show no such causation: Slight changes of the training algorithm can yield solutions with larger norm that generalize better (Figure 2, (C) & Figure 1, Top right: onset epoch 0 vs 25/50).
Effect of BatchNormalization.
One would expect regularization to be ineffective when used in conjunction with BatchNormalization (BN) (ioffe2015batch, ), since BN makes the network’s output invariant to changes in the norm of its weights. However, it has been observed that, in practice, WD improves generalization even, or especially, when used with BN. Several authors (zhang2018three, ; hoffer2018norm, ; van2017l2, ) have observed that WD increases the effective learning rate , where is the learning rate at epoch and is the squarednorm of weights at epoch , by decreasing the weight norm, which increases the effective gradient noise, which promotes generalization (neelakantan2015adding, ; jastrzkebski2017three, ; hoffer2017train, ). However, in the sliding window experiment for regularization, we observe that networks with regularization applied around epoch 50, despite having smaller weight norm (Figure 2 (C), compare onset epoch 50 to onset epoch 0) and thus a higher effective learning rate, generalize poorly (Figure 1 Top Right: onset epoch 50 has a mean test accuracy increase of 0.24% compared to 1.92% for onset epoch 0). We interpret the latter (onset epoch 0) as having a higher effective learning rate during the critical period, while for the former (onset epoch 50) it was past its critical period. Thus, previous observations in the literature should be considered with more nuance: we contend that an increased effective learning rate induces generalization only insofar as it modifies the dynamics during the critical period, reinforcing the importance of studying when to regularize, in addition to how. In Figure 9 in the Appendix, we show that the initial effective learning rate correlates better with generalization (Pearson coefficient 0.96, pvalue < 0.001) than the final effective learning rate (Pearson coefficient 0.85, pvalue < 0.001).
Weight decay, Fisher and flatness.
Generalization for DNNs is often correlated with the flatness of the minima to which the network converges during training (hochreiter1997flat, ; li2018visualizing, ; keskar2016large, ; chaudhari2016entropy, ), where solutions corresponding to flatter minima seem to generalize better. In order to understand if the effect of regularization is to increase the flatness at convergence, we use the Fisher Information Matrix (FIM), which is a semidefinite approximation of the Hessian of the loss function (martens2014new, ) and thus a measure of the curvature of the loss landscape. We recall that the Fisher Information Matrix is defined as:
In Figure 5 (Left) we plot the trace of FIM against the final accuracy. Notice that, contrary to our expectations, weight decay increases the FIM norm, and hence curvature of the convergence point, but this still leads to better generalization. Moreover, the effect of weight decay on the curvature is more marked during the transient (Figure 5).
This suggests that the peak curvature reached during the transient, rather than its final value, may correlate with the effectiveness of regularization. To test this hypothesis, we consider the DNNs trained in Figure 1 (Top) and plot the relationship between peak/final FIM value and test accuracy in Figure 5 (Center, Right): Indeed, while the peak value of the FIM strongly correlates with the final test performance (Pearson coefficient 0.92, pvalue < 0.001), the final value of the FIM norm does not (Pearson 0.29, pvalue > 0.05). We report plots of the Fisher Norm for delayed/sliding window application of WD in the Appendix (Figure 7).
The FIM was also used to study critical period for changes in the data distribution in achille2017critical
, which however in their setting observe an anticorrelation between Fisher and generalization. Indeed, the relationship between the flatness of the convergence point and generalization established in the literature emerges as rather complex, and we may hypothesize a more complex biasvariance tradeoff like a connection between the two, where either too low or too high curvature can be detrimental.
Jacobian norm.
(zhang2018three, ) relates the effect of regularization to the norm of the GaussNewton matrix, , where is the Jacobian of w.r.t , which in turn relates to norm of the networks inputoutput Jacobian. The Fisher Information Matrix is indeed related to the GN matrix (more precisely, it coincides with the generalized GaussNewton matrix, , where is the Hessian of w.r.t. ). However, while the GN norm remains approximately constant during training, we found the changes of the FisherNorm during training (and in particular its peak) to be informative of the critical period for regularization, allowing for a more detailed analysis.
5 Discussion and Conclusions
We have tested the hypothesis that there exists a “critical period” for regularization in training deep neural networks. Unlike classical machine learning, where regularization trades off the training error in the loss being minimized, DNNs are not subject to this tradeoff: One can train a model with sufficient capacity to zero training error regardless of the norm constraint imposed on the weights. Yet, weight decay works, even in the case where it seems it should not, for instance when the network is invariant to the scale of the weights, e.g., in the presence of batch normalization. We believe the reason is that regularization affects the early epochs of training by biasing the solution towards regions that have good generalization properties. Once there, there are many local extrema to which the optimization can converge. Which to is unimportant: Turning the regularizer on or off changes the loss function, and the optimizer moves accordingly, but test error is unaffected, at least for the variety of architectures, training sets, and learning rates we tested.
We believe that there are universal phenomena at play, and what we observe is not the byproduct of accidental choices of training set, architecture, and hyperparameters: One can see the absence of regularization as a learning deficit, and it has been known for decades that deficits that interfere with the early phases of learning, or critical periods, have irreversible effects, from humans to songbirds and, as recently shown by achille2017critical , deep neural networks. Critical periods depend on the type of deficits, the task, the species or architecture. We have shown results for two datasets, two architectures, two learning rate schedules.
While our exploration is by no means exhaustive, it supports the point that considerably more effort should be devoted to the analysis of the transient dynamics of Deep Learning. To this date, most of the theoretical work in Deep Learning focuses on the asymptotics and the properties of the minimum at convergence.
Our hypothesis also stands when considering the interaction with other forms of generalized regularization, such as batch normalization, and explains why weight decay still works, even though batch normalization makes the activations invariant to the norm of the weights, which challenges previous explanation of the mechanisms of action of weight decay.
We note that there is no tradeoff between regularization and loss in DNNs, and the effects of regularization cannot (solely) be to change the shape of the loss landscape (WD), or to change the variety of gradient noise (DA) preventing the network from converging to some local minimizers, as without regularization in the end, everything works. The main effect of regularization ought to be on the transient dynamics before convergence.
At present, there is no viable theory on transient regularization. The empirical results we present should be a call to arms for theoreticians interested in understanding Deep Learning. A possible interpretation advanced by achille2017critical is to interpret critical periods as the (irreversible) crossing of narrow bottlenecks in the loss landscape. Increasing the noise – either by increasing the effective learning rate (WD) or by adding variety to the samples (DA) – may help the network cross the right bottlenecks while avoiding those leading to irreversibly suboptimal solutions. If this is the case, can better regularizers be designed for this task?
References
 [1] Alessandro Achille, Matteo Rovere, and Stefano Soatto. Critical learning periods in deep networks. In International Conference on Learning Representations, 2019.
 [2] ShunIchi Amari. Natural gradient works efficiently in learning. Neural computation, 10(2):251–276, 1998.

[3]
Siegfried Bos and E Chug.
Using weight decay to optimize the generalization ability of a perceptron.
In Proceedings of International Conference on Neural Networks (ICNN’96), volume 1, pages 241–246. IEEE, 1996.  [4] Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, and Riccardo Zecchina. Entropysgd: Biasing gradient descent into wide valleys. arXiv preprint arXiv:1611.01838, 2016.
 [5] Anna Choromanska, Mikael Henaff, Michael Mathieu, Gérard Ben Arous, and Yann LeCun. The loss surfaces of multilayer networks. In Artificial Intelligence and Statistics, pages 192–204, 2015.
 [6] Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio. Identifying and attacking the saddle point problem in highdimensional nonconvex optimization. In Advances in neural information processing systems, pages 2933–2941, 2014.
 [7] Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio. Sharp minima can generalize for deep nets. In Proceedings of the 34th International Conference on Machine LearningVolume 70, pages 1019–1028. JMLR. org, 2017.
 [8] Ronald Aylmer Fisher. Theory of statistical estimation. In Mathematical Proceedings of the Cambridge Philosophical Society, volume 22, pages 700–725. Cambridge University Press, 1925.
 [9] Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 249–256, 2010.
 [10] Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. 2016.

[11]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.
Deep residual learning for image recognition.
In
Proceedings of the IEEE conference on computer vision and pattern recognition
, pages 770–778, 2016.  [12] Mikael Henaff, Arthur Szlam, and Yann LeCun. Recurrent orthogonal networks and longmemory tasks. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 1924, 2016, pages 2034–2042, 2016.
 [13] Sepp Hochreiter and Jürgen Schmidhuber. Flat minima. Neural Computation, 9(1):1–42, 1997.
 [14] Elad Hoffer, Ron Banner, Itay Golan, and Daniel Soudry. Norm matters: efficient and accurate normalization schemes in deep networks. In Advances in Neural Information Processing Systems, pages 2160–2170, 2018.
 [15] Elad Hoffer, Itay Hubara, and Daniel Soudry. Train longer, generalize better: closing the generalization gap in large batch training of neural networks. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 1731–1741. Curran Associates, Inc., 2017.
 [16] Elad Hoffer, Itay Hubara, and Daniel Soudry. Train longer, generalize better: closing the generalization gap in large batch training of neural networks. In Advances in Neural Information Processing Systems, pages 1731–1741, 2017.
 [17] ByungWoo Hong, JaKeoung Koo, Martin Burger, and Stefano Soatto. Adaptive regularization of some inverse problems in image analysis. arXiv preprint arXiv:1705.03350, 2017.
 [18] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32Nd International Conference on International Conference on Machine Learning  Volume 37, ICML’15, pages 448–456. JMLR.org, 2015.
 [19] Stanisław Jastrzębski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos Storkey. Three factors influencing minima in sgd. arXiv preprint arXiv:1711.04623, 2017.
 [20] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On largebatch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836, 2016.
 [21] Alex Krizhevsky. Learning multiple layers of features from tiny images. Technical report, Citeseer, 2009.
 [22] Anders Krogh and John A Hertz. A simple weight decay can improve generalization. In Advances in neural information processing systems, pages 950–957, 1992.
 [23] Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. Visualizing the loss landscape of neural nets. In Advances in Neural Information Processing Systems, pages 6389–6399, 2018.
 [24] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.
 [25] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019.
 [26] James Martens. New insights and perspectives on the natural gradient method. arXiv preprint arXiv:1412.1193, 2014.
 [27] Hossein Mobahi and John W Fisher III. A theoretical analysis of optimization by gaussian continuation. In TwentyNinth AAAI Conference on Artificial Intelligence, 2015.
 [28] Arvind Neelakantan, Luke Vilnis, Quoc V Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, and James Martens. Adding gradient noise improves learning for very deep networks. arXiv preprint arXiv:1511.06807, 2015.
 [29] Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro. A PACbayesian approach to spectrallynormalized margin bounds for neural networks. In International Conference on Learning Representations, 2018.
 [30] Leslie N Smith. Cyclical learning rates for training neural networks. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 464–472. IEEE, 2017.
 [31] Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806, 2014.
 [32] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958, 2014.
 [33] Twan van Laarhoven. L2 regularization versus batch and weight normalization. arXiv preprint arXiv:1706.05350, 2017.

[34]
Vladimir N Vapnik.
The vicinal risk minimization principle and the svms.
In
The nature of statistical learning theory
, pages 267–290. Springer, 2000.  [35] Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, and Benjamin Recht. The marginal value of adaptive gradient methods in machine learning. In Advances in Neural Information Processing Systems, pages 4148–4158, 2017.
 [36] Guodong Zhang, Chaoqi Wang, Bowen Xu, and Roger Grosse. Three mechanisms of weight decay regularization. In International Conference on Learning Representations, 2019.
Appendix A Details of the Experiments
We use the standard ResNet18 architecture [11] in all the experiments, stated unless otherwise. We train all the networks using SGD (momentum = 0.9) with a batchsize of 128 for 200 epochs, except when regularization is applied late during the training, where we train for an extra 50 epochs, to ensure the convergence of the networks. For experiments with ResNet18, we use an initial learning rate of 0.1, with learning rate decay factor of 0.97 per epoch and a weight decay coefficient of 0.0005. In the piecewise constant learning rate experiment, we use an initial learning rate of 0.1 and decay it by a factor of 0.1 every 60 epochs. While in the constant learning experiment we fix it to 0.001. For the AllCNN [31] experiments, we use an initial learning rate of 0.05 with a weight decay coefficient of 0.001 and a learning rate decay of 0.97 per epoch. For AllCNN, we do not use dropout and instead we add BatchNormalization to all layers.
a.1 Path Plotting
We follow the method proposed by [23] to plot the training trajectories of the DNNs for varying duration of regularization (Figure 2 A). More precisely, we combine the weights of the network (stored at regular intervals during the training) for different duration of regularization into a single matrix and then project them on the first two principal components of .
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