Title: EQUI-NORMALIZATION OF NEURAL NETWORKS

Abstract: Modern neural networks are over-parametrized. In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and output weights, without changing the rest of the network. Inspired by the Sinkhorn-Knopp algorithm, we introduce a fast iterative method for minimizing the 2 norm of the weights, equivalently the weight decay regularizer. It provably converges to a unique solution. Interleaving our algorithm with SGD during training improves the test accuracy. For small batches, our approach offers an alternative to batchand group-normalization on CIFAR-10 and ImageNet with a ResNet-18.

1. INTRODUCTION:

Deep Neural Networks (DNNs) have achieved outstanding performance across a wide range of empirical tasks such as image classification (Krizhevsky et al., 2012) , image segmentation (He et al., 2017) , speech recognition (Hinton et al., 2012a) , natural language processing (Collobert et al., 2011) or playing the game of Go (Silver et al., 2017) . These successes have been driven by the availability of large labeled datasets such as ImageNet (Russakovsky et al., 2015) , increasing computational power and the use of deeper models (He et al., 2015b) .

Although the expressivity of the function computed by a neural network grows exponentially with depth (Pascanu et al., 2013; Raghu et al., 2017; Telgarsky, 2016) , in practice deep networks are vulnerable to both over-and underfitting (Glorot & Bengio, 2010; Krizhevsky et al., 2012; He et al., 2015b) . Widely used techniques to prevent DNNs from overfitting include regularization methods such as weight decay (Krogh & Hertz, 1992) , Dropout (Hinton et al., 2012b) and various data augmentation schemes (Krizhevsky et al., 2012; Simonyan & Zisserman, 2014; Szegedy et al., 2014; He et al., 2015b) . Underfitting can occur if the network gets stuck in a local minima, which can be avoided by using stochastic gradient descent algorithms (Bottou, 2010; Duchi et al., 2011; Sutskever et al., 2013; Kingma & Ba, 2014) , sometimes along with carefully tuned learning rate schedules (He et al., 2015b; Goyal et al., 2017) .

Training deep networks is particularly challenging due to the vanishing/exploding gradient problem. It has been studied for Recurrent Neural networks (RNNs) (Hochreiter et al., 2001) as well as standard feedforward networks (He et al., 2015a; Mishkin & Matas, 2015) . After a few iterations, the gradients computed during backpropagation become either too small or too large, preventing the optimization scheme from converging. This is alleviated by using non-saturating activation functions such as rectified linear units (ReLUs) (Krizhevsky et al., 2012) or better initialization schemes preserving the variance of the input across layers (Glorot & Bengio, 2010; Mishkin & Matas, 2015; He et al., 2015a) . Failure modes that prevent the training from starting have been theoretically studied by Hanin & Rolnick (2018) .

Two techniques in particular have allowed vision models to achieve "super-human" accuracy. Batch Normalization (BN) was developed to train Inception networks (Ioffe & Szegedy, 2015) . It introduces intermediate layers that normalize the features by the mean and variance computed within the current batch. BN is effective in reducing training time, provides better generalization capabilities after training and diminishes the need for a careful initialization. Network architectures such as ResNet (He et al., 2015b) and DenseNet (Huang et al., 2016) use skip connections along with BN to improve the information flow during both the forward and backward passes.

. . . " # . . . " # " # " # W k ⇥ 1 ↵ nk ⇥↵ nk ⇥ 1 ↵ 1 ⇥↵ 1 ⇥ 1 ↵ 2 ⇥↵ 2 ⇥1/↵ 2 ⇥1/↵ 1 ⇥1/↵ nk ⇥1/ nk ⇥1/ 1 ⇥1/ 2 ⇥ 2 ⇥ 1 ⇥ 1 nk ⇥ n k ⇥ 1 1 ⇥ 1 2 W k+1

Figure 1 : Matrices W k and W k+1 are updated by multiplying the columns of the first matrix with rescaling coefficients. The rows of the second matrix are inversely rescaled to ensure that the product of the two matrices is unchanged. The rescaling coefficients are strictly positive to ensure functional equivalence when the matrices are interleaved with ReLUs. This rescaling is applied iteratively to each pair of adjacent matrices. In this paper, we address the more complex cases of biases, convolutions, max-pooling or skip-connections to be able to balance modern CNN architectures.

However, BN has some limitations. In particular, BN only works well with sufficiently large batch sizes (Ioffe & Szegedy, 2015; Wu & He, 2018) . For sizes below 16 or 32, the batch statistics have a high variance and the test error increases significantly. This prevents the investigation of highercapacity models because large, memory-consuming batches are needed in order for BN to work in its optimal range. In many use cases, including video recognition (Carreira & Zisserman, 2017) and image segmentation (He et al., 2017) , the batch size restriction is even more challenging because the size of the models allows for only a few samples per batch. Another restriction of BN is that it is computationally intensive, typically consuming 20% to 30% of the training time. Variants such as Group Normalization (GN) (Wu & He, 2018) cover some of the failure modes of BN.

In this paper, we introduce a novel algorithm to improve both the training speed and generalization accuracy of networks by using their over-parameterization to regularize them. In particular, we focus on neural networks that are positive-rescaling equivalent (Neyshabur et al., 2015) , i.e. whose weights are identical up to positive scalings and matching inverse scalings. The main principle of our method, referred to as Equi-normalization (ENorm), is illustrated in Figure 1 for the fullyconnected case. We scale two consecutive matrices with rescaling coefficients that minimize the joint p norm of those two matrices. This amounts to re-parameterizing the network under the constraint of implementing the same function. We conjecture that this particular choice of rescaling coefficients ensures a smooth propagation of the gradients during training.

A limitation is that our current proposal, in its current form, can only handle learned skipconnections like those proposed in type-C ResNet. For this reason, we focus on architectures, in particular ResNet18, for which the learning converges with learned skip-connection, as opposed to architectures like ResNet-50 for which identity skip-connections are required for convergence.

In summary,

• We introduce an iterative, batch-independent algorithm that re-parametrizes the network within the space of rescaling equivalent networks, thus preserving the function implemented by the network; • We prove that the proposed Equi-normalization algorithm converges to a unique canonical parameterization of the network that minimizes the global p norm of the weights, or equivalently, when p = 2, the weight decay regularizer; • We extend ENorm to modern convolutional architectures, including the widely used ResNets, and show that the theoretical computational overhead is lower compared to BN (×50) and even compared to GN (×3); • We show that applying one ENorm step after each SGD step outperforms both BN and GN on the CIFAR-10 (fully connected) and ImageNet (ResNet-18) datasets. • Our code is available at https://github.com/facebookresearch/enorm.

The paper is organized as follows. Section 2 reviews related work. Section 3 defines our Equinormalization algorithm for fully-connected networks and proves the convergence. Section 4 shows how to adapt ENorm to convolutional neural networks (CNNs). Section 5 details how to employ ENorm for training neural networks and Section 6 presents our experimental results.

2. RELATED WORK:

This section reviews methods improving neural network training and compares them with ENorm. Since there is a large body of literature in this research area, we focus on the works closest to the proposed approach. From early works, researchers have noticed the importance of normalizing the input of a learning system, and by extension the input of any layer in a DNN (LeCun et al., 1998) . Such normalization is applied either to the weights or to the activations. On the other hand, several strategies aim at better controlling the geometry of the weight space with respect to the loss function. Note that these research directions are not orthogonal. For example, explicitly normalizing the activations using BN has smoothing effects on the optimization landscape (Santurkar et al., 2018) .

Normalizing activations. Batch Normalization (Ioffe & Szegedy, 2015) normalizes the activations by using statistics computed along the batch dimension. As stated in the introduction, the dependency on the batch size leads BN to underperform when small batches are used. Batch Renormalization (BR) (Ioffe, 2017 ) is a follow-up that reduces the sensitivity to the batch size, yet does not completely alleviate the negative effect of small batches. Several batch-independent methods operate on other dimensions, such as Layer Normalization (channel dimension) (Ba et al., 2016) and Instance-Normalization (sample dimension) (Ulyanov et al., 2016) . Parametric data-independent estimation of the mean and variance in every layer is investigated by Arpit et al. (2016) . However, these methods are inferior to BN in standard classification tasks. More recently, Group Normalization (GN) (Wu & He, 2018) , which divides the channels into groups and normalizes independently each group, was shown to effectively replace BN for small batch sizes on computer vision tasks.

Normalizing weights. Early weight normalization techniques only served to initialize the weights before training (Glorot & Bengio, 2010; He et al., 2015a) . These methods aim at keeping the variance of the output activations close to one along the whole network, but the assumptions made to derive these initialization schemes may not hold as training evolves. More recently, Salimans & Kingma (2016) propose a polar-like re-parametrization of the weights to disentangle the direction from the norm of the weight vectors. Note that Weight Norm (WN) does require mean-only BN to get competitive results, as well as a greedy layer-wise initialization as mentioned in the paper.

Optimization landscape. Generally, in the parameter space, the loss function moves quickly along some directions and slowly along others. To account for this anisotropic relation between the parameters of the model and the loss function, natural gradient methods have been introduced (Amari, 1998) . They require storing and inverting the N × N curvature matrix, where N is the number of network parameters. Several works approximate the inverse of the curvature matrix to circumvent this problem (Pascanu & Bengio, 2013; Marceau-Caron & Ollivier, 2016; Martens & Grosse, 2015) . Another method called Diagonal Rescaling (Lafond et al., 2017) proposes to tune a particular reparametrization of the weights with a block-diagonal approximation of the inverse curvature matrix. Finally, Neyshabur et al. (2015) propose a rescaling invariant path-wise regularizer and use it to derive Path-SGD, an approximate steepest descent with respect to the path-wise regularizer.

Positioning. Unlike BN, Equi-normalization focuses on the weights and is independent of the concept of batch. Like Path-SGD, our goal is to obtain a balanced network ensuring a good backpropagation of the gradients, but our method explicitly re-balances the network using an iterative algorithm instead of using an implicit regularizer. Moreover, ENorm can be readily adapted to the convolutional case whereas Neyshabur et al. (2015) restrict themselves to the fully-connected case. In addition, the theoretical computational complexity of our method is much lower than the overhead introduced by BN or GN (see Section 5). Besides, WN parametrizes the weights in a polar-like manner, w = g × v/|v|, where g is a scalar and v are the weights, thus it does not balance the network but individually scales each layer. Finally, Sinkhorn's algorithm aims at making a single matrix doubly stochastic, while we balance a product of matrices to minimize their global norm.

3. EQUI-NORMALIZATION:

We first define Equi-normalization in the context of simple feed forward networks that consist of two operators: linear layers and ReLUs. The algorithm is inspired by Sinkhorn-Knopp and is designed to balance the energy of a network, i.e., the p -norm of its weights, while preserving its function. When not ambiguous, we may denote by network a weight parametrization of a given network architecture.

3.1. NOTATION AND DEFINITIONS:

We consider a network with q linear layers, whose input is a row vector x ∈ R n0 . We denote by σ the point-wise ReLU activation. For the sake of exposition, we omit a bias term at this stage. We recursively define a simple fully connected feedforward neural network with L layers by y

EQUATION

and y q = y q-1 W q . Each linear layer k is parametrized by a matrix W k ∈ R n k-1 ×n k . We denote by f θ (x) = y q the function implemented by the network, where θ is the concatenation of all the network parameters. We denote by D(n) the set of diagonal matrices of size n × n for which all diagonal elements are strictly positive and by I n the identity matrix of size n × n.

Definition 1. θ and θ are functionally equivalent if, for all

x ∈ R n0 , f θ (x) = f θ (x).

Definition 2. θ and θ are rescaling equivalent if, for all k ∈ 1, q -1 , there exists a rescaling matrix

D k ∈ D(n k ) such that, for all k ∈ 1, q -1 , W k = D -1 k-1 W k D k

(2) with the conventions that D 0 = I n0 and D q = I nq . This amounts to positively scaling all the incoming weights and inversely scaling all the outgoing weights for every hidden neuron.

Two weights vectors θ and θ that are rescaling equivalent are also functionally equivalent (see Section 3.5 for a detailed derivation). Note that a functional equivalence class is not entirely described by rescaling operations. For example, permutations of neurons inside a layer also preserve functional equivalence, but do not change the gradient. In what follows our objective is to seek a canonical parameter vector that is rescaling equivalent to a given parameter vector. The same objective under a functional equivalence constraint is beyond the scope of this paper, as there exist degenerate cases where functional equivalence does not imply rescaling equivalence, even up to permutations.

3.2. OBJECTIVE FUNCTION: CANONICAL REPRESENTATION:

Given a network f θ and p > 0, we define the p norm of its weights as p (θ) = q k=1 W k p p . We are interested in minimizing p inside an equivalence class of neural networks in order to exhibit a unique canonical element per equivalence class. We denote the rescaling coefficients within the network as

d k ∈ (0, +∞) n k for k ∈ 1, q -1 or as diagonal matrices D k = Diag(d k ) ∈ D(n k ).

We denote δ = (d 1 , . . . , d q-1 ) ∈ R n , where n is the number of hidden neurons. Fixing the weights {W k }, we refer to {D -1 k-1 W k D k } as the rescaled weights, and seek to minimize their p norm as a function of the rescaling coefficients:

ϕ(δ) = q k=1 D -1 k-1 W k D k p p .

(3)

3.3. COORDINATE DESCENT: ENORM ALGORITHM:

We formalize the ENorm algorithm using the framework of block coordinate descent. We denote by

W [:, j] (resp. W k [i, :]) the j th column (resp. i th row) of a matrix W k .

In what follows we assume that each hidden neuron is connected to at least one input and one output neuron. ENorm generates a sequence of rescaling coefficients δ (r) obtained by the following steps.

(1) Initialization. Define δ (0) = (1, . . . , 1).

(2) Iteration. At iteration r, consider layer ∈ 1, q -1 such that -1 ≡ r mod q -1 and define

EQUATION

q-1 .

Denoting uv the coordinate-wise product of two vectors and u/v for the division, we have

EQUATION

)

Algorithm 1: Pseudo-code of Equi-normalization Input: Current layer weights W 1 , . . . , W q , number of cycles C, choice of norm p Output: Balanced layer weights // Perform T ENorm cycles for t = 1 . . . T do // Iterate through the layers

for k = 2 . . . q do L[j] ←-W k-1 [:, j] p for all j ∈ R n k R[i] ←-W k [i, :] p for all i ∈ R n k D k-1 ←-Diag R/L W k-1 ←-W k-1 D k-1 W k ←-(D k-1 ) -1 W k

Algorithm and pseudo-code. Algorithm 1 gives the pseudo-code of ENorm. By convention, one ENorm cycle balances the entire network once from = 1 to = q -1. See Appendix A for illustrations showing the effect of ENorm on network weights.

3.4. CONVERGENCE:

We now state our main convergence result for Equi-normalization. The proof relies on a coordinate descent Theorem by Tseng ( 2001) and can be found in Appendix B.1. The main difficulty is to prove the uniqueness of the minimum of ϕ. Theorem 1. Let p > 0 and (δ (r) ) r∈N be the sequence of rescaling coefficients generated by ENorm from the starting point δ (0) as described in Section 3.3. We assume that each hidden neuron is connected to at least one input and one output neuron. Then,

(1) Convergence. The sequence of rescaling coefficients δ (r) converges to δ * as r → +∞. As a consequence, the sequence of rescaled weights also converges;

(2) Minimum global p norm. The rescaled weights after convergence minimize the global p norm among all rescaling equivalent weights;

(3) Uniqueness. The minimum is unique, i.e. δ * does not depend on the starting point δ (0) .

3.5. HANDLING BIASES -FUNCTIONAL EQUIVALENCE:

In the presence of biases, the network is defined as

y k = σ (y k-1 W k + b k ) and y q = y q-1 W q + b k where b k ∈ R n k .

For rescaling-equivalent weights satisfying (2), in order to preserve the inputoutput function, we define matched rescaling equivalent biases b k = b k D k . In Appendix B.2, we show by recurrence that for every layer k,

EQUATION

where y k (resp. y k ) is the intermediary network function associated with the weights W (resp. W ). In particular, y q = y q , i.e. rescaling equivalent networks are functionally equivalent. We also compute the effect of applying ENorm on the gradients in the same Appendix.

3.6. ASYMMETRIC SCALING:

Equi-normalization is easily adapted to introduce a depth-wise penalty on each layer. We consider the weighted loss p,(c1,...,cq) (θ) = q k=1 c k W k p . This amounts to modifiying the rescaling coefficients as d(r+1

) [i] = d (r+1) [i] (c k+1 /c k ) 1/2p .

(6) In Section 6, we explore two natural ways of defining c k : c k = c p(q-k) (uniform) and c k = 1/(n k-1 n k ) (adaptive). In the uniform setup, we penalize layers exponentially according to their depth: for instance, values of c larger than 1 increase the magnitude of the weights at the end of the network. In the adaptive setup, the loss is weighted by the size of the matrices.

4. EXTENSION TO CNNS:

We now extend ENorm to CNNs, by focusing on the typical ResNet architecture. We briefly detail how we adapt ENorm to convolutional or max-pooling layers, and then how to update an elementary block with a skip-connection. We refer the reader to Appendix C for a more extensive discussion. Sanity checks of our implementation are provided in Appendix E.1.

4.1. CONVOLUTIONAL LAYERS:

Figure 2 explains how to rescale two consecutive convolutional layers. As detailed in Appendix C, this is done by first properly reshaping the filters to 2D matrices, then performing the previously described rescaling on those matrices, and then reshaping the matrices back to convolutional filters. This matched rescaling does preserve the function implemented by the composition of the two layers, whether they are interleaved with a ReLU or not. It can be applied to any two consecutive convolutional layers with various stride and padding parameters. Note that when the kernel size is 1 in both layers, we recover the fully-connected case of Figure 1 .

4.2. MAX-POOLING:

The MaxPool layer operates per channel by computing the maximum within a fixed-size kernel. We adapt Equation ( 5) to the convolutional case where the rescaling matrix D k is applied to the channel dimension of the activations y k . Then,

EQUATION

Thus, the activations before and after the MaxPool layer have the same scaling and the functional equivalence is preserved when interleaving convolutional layers with MaxPool layers.

4.3. SKIP-CONNECTION:

We now consider an elementary block of a ResNet-18 architecture as depicted in Figure 3 . In order to maintain functional equivalence, we only consider ResNet architectures of type C as defined in (He et al., 2015b) , where all shortcuts are learned 1 × 1 convolutions. As detailed in Appendix C, rescaling two consecutive blocks requires (a) to define the structure of the rescaling process, i.e.

where to insert the rescaling coefficients and (b) a formula for computing those rescaling coefficients.

5. TRAINING PROCEDURE: EQUI-NORMALIZATION & SGD:

ENorm & SGD. As detailed in Algorithm 2, we balance the network periodically after updating the gradients. By design, this does not change the function implemented by the network but will yield different gradients in the next SGD iteration. Because this re-parameterization performs a jump in the parameter space, we update the momentum using Equation ( 17) and the same matrices D k as those used for the weights. The number of ENorm cycles after each SGD step is an hyperparameter and by default we perform one ENorm cycle after each SGD step. In Appendix D, we also explore a method to jointly learn the rescaling coefficients and the weights with SGD, and report corresponding results.

Computational advantage over BN and GN. Table 1 provides the number of elements (weights or activations) accessed when normalizing using various techniques. Assuming that the complexity (number of operations) of normalizing is proportional to the number of elements and assuming all techniques are equally parallelizable, we deduce that our method (ENorm) is theoretically 50 times faster than BN and 3 times faster than GN for a ResNet-18. In terms of memory, ENorm requires no extra-learnt parameters, but the number of parameters learnt by BN and GN is negligible (4800 for a ResNet-18 and 26,650 for a ResNet-50). We implemented ENorm using a tensor library; to take full advantage of the theoretical reduction in compute would require an optimized CUDA kernel.

. . . . . .

Layer k C k filters C k 1 ⇥ S k ⇥ S k Layer k + 1 C k+1 filters C k ⇥ S k+1 ⇥ S k+1 ⇥ 2 ⇥ 1 ⇥ 1 n k ⇥ n k ⇥ 1 1 ⇥ 1 2 Figure 2:

Rescaling the weights of two consecutive convolutional layers that preserves the function implemented by the CNN. Layer k scales channel number i of the input activations by γ i and layer k+1 cancels this scaling with the inverse scalar so that the activations after layer k+1 are unchanged. 

6. EXPERIMENTS:

We analyze our approach by carrying out experiments on the MNIST and CIFAR-10 datasets and on the more challenging ImageNet dataset. ENorm will refer to Equi-normalization with p = 2.

6.1. MNIST AUTO-ENCODER:

Training. We follow the setup of Desjardins et al. (2015) . Input data is normalized by subtracting the mean and dividing by standard deviation. The encoder has the structure FC(784, 1000)-ReLU-FC(1000, 500)-ReLU-FC(500, 250)-ReLU-FC(250, 30) and the decoder has the symmetric structure. We use He's initialization for the weights. We select the learning rate in {0.001, 0.01, 0.1} et al., 2015) and we set the weight decay to zero. For GN, we cross-validate the number of groups among {5, 10, 20, 50}. For WN, we use BN as well as a greedy layer-wise initialization as described in the original paper.

Results. While ENorm alone obtains competitive results compared to BN and GN, ENorm + BN outperforms all other methods, including WN + BN. Note that here ENorm refers to Enorm using the adaptive c parameter as described in Subsection 3.6, whereas for ENorm + BN we use the uniform setup with c = 1. We perform a parameter study for different values and setups of the asymmetric scaling (uniform and adaptive) in Appendix E.2. Without BN, the adaptive setup outperforms all other setups, which may be due to the strong bottleneck structure of the network. With BN, the dynamic is different and the results are much less sensitive to the values of c. Results without any normalization and with Path-SGD are not displayed because of their poor performance.

6.2. CIFAR-10 FULLY CONNECTED:

Training. We first experiment with a basic fully-connected architecture that takes as input the flattened image of size 3072. Input data is normalized by subtracting mean and dividing by standard deviation independently for each channel. The first linear layer is of size 3072 × 500. We then consider p layers 500 × 500, p being an architecture parameter for the sake of the analysis. The last classification is of size 500 × 10. The weights are initialized with He's scheme. We train for 60 epochs using SGD with no momentum, a batch size of 256 and weight decay of 10 -3 . Cross validation is used to pick an initial learning rate in {0.0005, 0.001, 0.005, 0.01, 0.05, 0.1}. Path-SGD, GN and WN are learned as detailed in Section 6.1. All results are the average test accuracies over 5 training runs.

Results. ENorm alone outperforms both BN and GN for any depth of the network. ENorm + BN outperforms all other methods, including WN + BN, by a good margin for more than p = 11 intermediate layers. Note that here ENorm as well as ENorm + BN refers to ENorm in the uniform setup with c = 1.2. The results of the parameter study for different values and setups of the asymmetric scaling are similar to those of the MNIST setup, see Appendix E.2.

6.3. CIFAR-10 FULLY CONVOLUTIONAL:

Training. We use the CIFAR-NV architecture as described by Gitman & Ginsburg (2017) . Images are normalized by subtracting mean and dividing by standard deviation independently for each channel. During training, we use 28 × 28 random crops and randomly flip the image horizontally.

At test time, we use 28 × 28 center crops. We split the train set into one training set (40,000 images) and one validation set (10,000 images). We train for 128 epochs using SGD and an initial learning rate cross-validated on a held-out set among {0.01, 0.05, 0.1}, along with a weight decay of 0.001. The learning rate is then decayed quadratically to 10 -4 . We compare various batch sizes together with the use of momentum (0.9) or not. The weights are initialized with He's scheme. In order to stabilize the training, we employ a BatchNorm layer at the end of the network after the FC layer for the Baseline and ENorm cases. For GN we cross-validate the number of groups among {4, 8, 16, 32, 64}. All results are the average test accuracies over 5 training runs. Training. Our experimental setup closely follows that of GN (Wu & He, 2018) . We train on 8 GPUs and compute the batch mean and standard deviation per GPU when evaluating BN. We use the Kaiming initialization for the weights (He et al., 2015a) and the standard data augmentation scheme of Szegedy et al. (2014) . We train our models for 90 epochs using SGD with a momentum of 0.9. We adopt the linear scaling rule for the learning rate (Goyal et al., 2017) and set the initial learning rate to 0.1B/256 where the batch size B is set to 32, 64, 128, or 256. As smaller batches lead to more iterations per epoch, we adopt a similar rule and adopt a weight decay of w = 10 -4 for B = 128 and 256, w = 10 -4.5 for B = 64 and w = 10 -5 for B = 32. We decay the learning rate quadratically (Gitman & Ginsburg, 2017) to 10 -5 and report the median error rate on the final 5 epochs. When using GN, we set the number of groups G to 32 and did not cross-validate this value as prior work (Wu & He, 2018) reports little impact when varying G from 2 to 64. In order for the training to be stable and faster, we added a BatchNorm at the end of the network after the FC layer for the Baseline and ENorm cases. The batch mean and variance for this additional BN are shared across GPUs. Note that the activation size at this stage of the network is B × 1000, which is a negligible overhead (see Table 1 ).

Results. We compare the Top 1 accuracy on a ResNet-18 when using no normalization scheme, (Baseline), when using BN, GN and ENorm (our method). In both the Baseline and ENorm settings, we add a BN at the end of the network as described in 6.3. The results are reported in 

6.5. LIMITATIONS:

We applied ENorm to a deeper (ResNet-50), but obtained unsatisfactory results. We observed that learnt skip-connections, even initialized to identity, make it harder to train without BN, with careful layer-wise initialization or learning rate warmup. This would require further investigation.

7. CONCLUDING REMARKS:

We presented Equi-normalization, an iterative method that balances the energy of the weights of a network while preserving the function it implements. ENorm provably converges towards a unique equivalent network that minimizes the p norm of its weights and it can be applied to modern CNN architectures. Using ENorm during training adds a much smaller computational overhead than BN or GN and leads to competitive performances in the FC case as well as in the convolutional case.

Discussion. While optimizing an unbalanced network is hard (Neyshabur et al., 2015) , the criterion we optimize to derive ENorm is likely not optimal regarding convergence or training properties. These limitations suggest that further research is required in this direction. 0) be the starting point of the coordinate descent algorithm and X the level set X = x | f (x) ≤ f x (0) . We make the following assumptions:

(1) f is differentiable on D ;

(2) X is compact ;

(3) for each x ∈ D, each block coordinate function f : t → f (x 1 , . . . , x -1 , t, x +1 , . . . , x B ),

where 2 ≤ ≤ B -1, has at most one minimum.

Under these assumptions, the sequence (x (r) ) r∈N generated by the coordinate descent algorithm is defined and bounded. Moreover, every cluster point of (x (r) ) r∈N is a local minimizer of f . STEP 1. We apply Theorem 2 to the function ϕ. This is possible because all the assumptions are verified as shown below. Recall that

EQUATION

Assumption (1). ϕ is differentiable on the open set D = (0, +∞) n .

Assumption (2). ϕ → +∞ when δ → +∞. Let δ such that ϕ(δ) < M p , M > 1. Let us show by induction that for all k ∈ 1, q -1 , d k ∞ < (CM ) k , where C = max(C 0 , 1) and

EQUATION

• For the first hidden layer, index k = 1. By assumption, every hidden neuron is connected at least to one neuron in the input layer. Thus, for every j, there exists i such that W 1 [i, j] = 0.

Because ϕ(δ) < M p and d 0 [i] = 1 for all i,

(d 1 [j]) p |W 1 [i, j]| p = d 1 [j] d 0 [i] p |W 1 [i, j]| p < M p (10)

Thus d 1 ∞ < CM .

• For some hidden layer, index k. By assumption, every hidden neuron is connected at least to one neuron in the previous layer. Thus, for every j, there exists i such that

EQUATION

Using the induction hypothesis, we get

d k ∞ < (CM ) k . Thus, δ ∞ < (M C) q because M C > 1. By contraposition, ϕ → +∞.

Thus, there exists a ball B such that δ / ∈ B implies ϕ(δ) > ϕ(δ 0 ). Thus, δ ∈ X implies that x ∈ B and X ⊂ B is bounded. Moreover, X is closed because ϕ is continuous thus X is compact and Assumption (2) is satisfied.

Assumption (3). We next note that

ϕ l (t) = ϕ d (r) 1 , . . . , d (r) -1 , t, d (r) +1 , . . . , d (r) q-1 (12)

has a unique minimum as shown in Section 3.3, see Equation ( 4). The existence and uniqueness of the minimum comes from the fact that each hidden neuron is connected to at least one input and one output neuron, thus all the row and column norms of the hidden weight matrices W k are non-zero, as well as the column (resp. row) norms or W 1 (resp. W q ). STEP 2. We prove that ϕ has at most one stationary point on D under the assumption that each hidden neuron is connected either to an input neuron or to an output neuron, which is weaker than the general assumption of Theorem 1.

We first introduce some definitions. We denote the set of all neurons in the network by V . Each neuron ν ∈ V belongs to a layer k ∈ 0, q and has an index i ∈ 1, n k in this layer. Any edge e connects some neuron i at layer k -1 to some neuron j at layer k, e = (k, i, j). We further denote by H the set of hidden neurons ν belonging to layers q ∈ 1, q -1 . We define E as the set of edges whose weights are non-zero, i.e.

EQUATION

For each neuron ν, we define prev(ν) as the neurons connected to ν that belong to the previous layer.

We now show that ϕ has at most one stationary point on D. Directly computing the gradient of ϕ and solving for zeros happens to be painful or even intractable. Thus, we define a change of variables as follows. We define h as h : (0, +∞)

H → R H δ → log(δ)

We next define the shift operator S : R V → R E such that, for every

x ∈ R V , S(x) = (ν -ν ) ν,ν ∈V s.t. ν ∈prev(ν)

and the padding operator P as

P : R H → R V x → y where y ν = 0 if ν ∈ V \H; y ν = x ν otherwise.

We define the extended shift operator S H = S • P . We are now ready to define our change of variables. We define χ = ψ STEP 3. We prove that the sequence δ (r) converges.

Step 1 implies that the sequence δ (r) is bounded and has at least one cluster point, as f is continuous on the compact X.

Step 2 implies that the sequence δ (r) has at most one cluster point. We then use the fact that any bounded sequence with exactly one cluster point converges to conclude the proof.

S IS INJECTIVE. Let x ∈ ker S H . Let us show by induction on the hidden layer index k that for every neuron ν at layer k, x ν = 0.

• k = 1. Let ν be a neuron at layer 1. Then, there exists a path coming from an input neuron to ν 0 through edge e 1 . By definition, P (x) ν0 = 0 and P (x) ν = x ν , hence S H (x) e1 = x ν -0. Since S H (x) = 0 it follows that x ν = 0. • k → k + 1. Same reasoning using the fact that x ν k = 0 by the induction hypothesis.

The case where the path goes from neuron ν to some output neuron is similar.

B.2 FUNCTIONAL EQUIVALENCE:

We show (5) by induction that for every layer k, i.e.,

y k = y k D k ,

where y k (resp. y k ) is the intermediary network function associated with weights W (resp. W ). This holds for k = 0 since D 0 = I n0 by convention. If the property holds for some k < q -1, then by (2) we have

y k W k+1 = y k D k W k+1 = y k W k+1 D k+1 hence, since b k+1 = b k+1 D k+1 , y k+1 = σ y k W k+1 + b k+1 = σ (y k W k+1 D k+1 + b k+1 D k+1 ) = σ (y k W k+1 + b k+1 ) D k+1 = y k+1 D k+1 .

The same equations hold for k = q -1 without the non-linearity σ.

Using the chain rule and denoting by the loss of the network, for every layer k, using (5), we have

EQUATION

Similarly, we obtain

EQUATION

Equation ( 17) will be used to update the momentum (see Section 5) and Equation ( 2) for the weights. 

EQUATION

For example, we first reshape T k as a 2D matrix by collapsing its last 3 dimensions, then transpose it to obtain M k . We then jointly rescale those 2D matrices using rescaling matrices D k ∈ D(k) as detailed in Section 3 and perform the inverse axis permutation and reshaping operations to obtain a right-rescaled weight tensor T k and a left-rescaled weight tensor T k+1 . See Figure 2 for an illustration of the procedure. This matched rescaling does preserve the function implemented by the composition of the two layers, whether they are interleaved with a ReLU or not. It can be applied to any two consecutive convolutional layers with various stride and padding parameters. Note that when the kernel size is 1 in both layers, we recover the fully-connected case of Figure 1 .

C.2 SKIP-CONNECTION:

We now consider an elementary block of a ResNet-18 architecture as depicted in Figure 3 . In order to maintain functional equivalence, we only consider ResNet architectures of type C as defined in (He et al., 2015b) , where all shortcuts are learned 1 × 1 convolutions.

Structure of the rescaling process. Let us consider a ResNet block k. We first left-rescale the Conv1 and ConvSkip weights using the rescaling coefficients calculated between blocks k -1 and k.

We then rescale the two consecutive layers Conv1 and Conv2 with their own specific rescaling coefficients, and finally right-rescale the Conv2 and ConvSkip weights using the rescaling coefficients calculated between blocks k and k + 1. We finally compare the evolution of the total 2 norm of the network when training it, with or without ENorm. We use the setup described in Subsection 6.2 and use p = 3 intermediary layers. The results are presented in Figure 10 . ENorm consistently leads to a lower energy level in the network. MNIST auto-encoder. For the uniform setup, we test for three different values of c, without BN: c = 1 (uniform setup), c = 0.8 (uniform setup), c = 1.2 (uniform setup). We also test the adaptive setup. The adaptive setup outperforms all other choices, which may be due to the strong bottleneck structure of the network. With BN, the dynamics are different and the results are much less sensitive to the values of c (see Figures 11 and 12 ). CIFAR10 Fully Convolutional. For the uniform setup, we test for three different values of c, without BN: c = 1 (uniform setup), c = 0.8 (uniform setup), c = 1.2 (uniform setup). We also test the adaptive setup (see Table 5 ). Once again, the dynamics with or without BN are quite different. With or without BN, c = 1.2 performs the best, which may be linked to the fact that the ReLUs are cutting energy during each forward pass. With BN, the results are less sensitive to the values of c. 

:

Note that what Tseng denotes as stationary point in his paper is actually defined as a point where the directional derivative is positive along every possible direction, i.e. a local minimum.

Figures and Tables:

Figure 3: Rescaling an elementary block within a ResNet-18 consists of 3 steps. (1) Conv1 and Con-vSkip are left-rescaled using the rescaling coefficients between blocks k -1 and k; (2) Conv1 and Conv2 are rescaled as two usual convolutional layers; (3) Conv2 and ConvSkip are right-rescaled using the rescaling coefficients between blocks k and k + 1. Identical colors denote the same rescaling coefficients D. Coefficients between blocks are rescaled as detailed in Section C.2.

Let D ⊂ R n be an open set and f : D → R a real function of B block variables x b ∈ R n b with B b=1 n b = n. Let x (

APPENDIX C EXTENSION OF ENORM TO CNNS C.1 CONVOLUTIONAL LAYERS Let us consider two consecutive convolutional layers k and k + 1, without bias. Layer k has C k filters of size C k-1 × S k × S k , where C k-1 is the number of input features and S k is the kernel size. This results in a weight tensor T k of size C k × C k-1 × S k × S k . Similarly, layer k + 1 has a weight matrix T k+1 of size C k+1 × C k × S k+1 × S k+1 . We then perform axis-permutation and reshaping operations to obtain the following 2D matrices:

Figure 9: Iterating ENorm cycles on a randomly initialized ResNet-18 with no training.

Figure 10: Training a fully-connected network on CIFAR-10, with (ENorm-1) or without (Baseline) Equi-normalization.

Figure 11: Uniform vs adaptive scaling on MNIST, without BN.

Figure 12: Uniform vs adaptive scaling on MNIST, with BN.

The Figure 6: ResNet-18 results on the ImageNet dataset, batch size 64.performance of BN decreases with small batches, which concurs with prior observations(Wu & He,  2018). Our method outperforms GN and BN for batch sizes ranging from 32 to 128. GN presents stable results across batch sizes. Note that values of c different from 1 did not yield better results.The training curves for a size of 64 are presented in Figure6. While BN and GN are faster to converge than ENorm, our method achieves better results after convergence in this case. Note also that ENorm overfits the training set less than BN and GN, but more than the Baseline case.

Table content: <table><tr><td>Batch size</td><td>32</td><td>64</td><td>128</td><td>256</td></tr><tr><td>Baseline</td><td colspan="4">66.20 68.60 69.20 69.58</td></tr><tr><td>BN</td><td colspan="4">68.01 69.38 70.83 71.37</td></tr><tr><td>GN</td><td colspan="4">68.94 68.90 70.69 70.64</td></tr><tr><td colspan="5">ENorm-1 (ours) 69.70 70.10 71.03 71.14</td></tr></table>

ResNet-18 results on the ImageNet dataset (test accuracy).

Table content: <table/>

Matus Telgarsky. Benefits of depth in neural networks. CoRR, 2016. P. Tseng. Convergence of a block coordinate descent method for nondifferentiable minimization. J.Optim. Theory Appl., 2001.

Table content: <table><tr><td>Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky. Instance normalization: The missing</td></tr><tr><td>ingredient for fast stylization. CoRR, 2016.</td></tr><tr><td>Yuxin Wu and Kaiming He. Group normalization. CoRR, abs/1803.08494, 2018.</td></tr></table>

As χ is the composition of a strictly convex function, ψ, and a linear injective function, S H (proof after Step 3), it is strictly convex. Thus χ has at most one stationary point, which concludes this step.

Table content: <table><tr><td/><td>p</td><td/></tr><tr><td>and observe that</td><td/><td/></tr><tr><td>so that its differential satisfies</td><td>ϕ = χ • h</td><td>(14)</td></tr><tr><td/><td>[Dϕ](δ) = [Dχ](h(δ))[Dh](δ).</td><td>(15)</td></tr></table>

Uniform vs adaptive scaling, CIFAR-10 fully convolutional

Table content: <table/>

Peer review (iclr2019):
 Summary: This paper introduces equi-normalization (ENorm): a normalization technique that relies on the scaling invariance properties of the ReLU, similarly to Path-SGD. Their method explicitly use this property to balance the weights of the network, without changing the function computed by the network. The main difference with Path-SGD is that the network is explicitly balanced, while Path-SGD uses a regularizer to implicitly balance the network. Since it doesn’t rely on mini-batch to normalize the network, Equi-normalization could be a good alternative to BN in small mini-batch regime. The method is validated on 3 tasks (MLP on CIFAR10, CNN on CIFAR10, Reidual Network on ImageNet). Clarity: The paper is quite clear, although a bit long (10 pages). The related work section is particularly nice. I really appreciated the “positioning” paragraph, which really explains how the method differs from others. Novelty: The paper is quite incremental, due to its similarities with Path-SGD. It seems quite close to what Weight Normalization is doing as well (see detailed comments). Pros and Cons: + Clearly written + Clearly motivated + Nice review of literature - Quite incremental (close to Path-SGD / Weight Normalization), and missing actual comparison with Weight Normalization, which seems to be the direct competitor of ENorm (see detailed comments) - Some flaws in the experimental setup (see detailed comments), particularly in the fully-connected experiment. - Doesn’t scale (yet) to deeper architectures, which is precisely where small batch sizes become a problem for BN and thus where ENorm would be needed. Detailed Comments: 1. Differences with Weight Normalization: I have trouble seeing the difference between the proposed method and Weight Normalization (WN), or the more advanced Normalization Propagation (NP). It seems that both methods are performing quite similar normalization: WN reparameterize the network such that ||W[:,j]||_2 = 1, so it seems that you wouldn’t need to re-balance the network when using WN. Could the authors elaborate on this? Moreover WN is simple to implement, fast, and also works in the small batches settings as well. Finally, since those methods are quite similar, the authors should compare their method against WN in their experimental setup. 2. Initialization of the Weights: The Xavier initialization you are using in the CIFAR10 experiments is designed to work with Tanh activations functions. For ReLUs, one should use the Kaiming initialization, which has been proven to work way better for ReLUs (He et al., 2015a). This certainly explains the poor performances of your baseline when you increase the number of layers in Figure 4, and probably explains why you need to add a BN layer at the end of your network to help with the training of the baselines. I suggest the authors to re-run the baselines using proper initialization for ReLUs. 3. Fully-Connected Layers Benchmark: I think the fully-connected benchmark you used is quite poor. A baseline with 1 layer only reaches ~54 % test accuracy and your method needs a 11 layer model to increase this baseline performance of about ~0.5 % only. The deep autoencoder on MNIST (see e.g. Desjardins et al., Natural neural networks, NIPS 2015), would probably be a better benchmark for fully-connected layers (of course using ReLUs in place of Sigmoids). It would also reinforce your empirical results by adding a 3rd dataset. 4. ImageNet Experiment: Do you use Ghost Batch Normalization in this experiments (i.e. calculating the BN statistics on each GPU separately)? It would certainly explain the poor performances of BN with tiny batch size (32 examples on 8 GPUs means only 8 examples per GPU). 5. Computation Time performances: It is stated in the conclusion that “using ENorm during training adds a much smaller computational overhead than BN or GN …”. I can see that Table 1 gives an overview of the number of elements accessed during normalization, but I do think that a proper plot showing the accuracy versus the wall-clock time would be a better way of showing how your method compares in practice with BN or GN. Moreover, as stated previously, comparison with WN (and / or NP) need to be performed as well, especially because WN and NP are also way faster to compute than BN and GN. 6. Shortening the paper: The recommended paper size for ICLR is 8 pages. Here would be a few pointers that could help you reduce a bit the length of the paper: Introduction and Related Work takes up 3 pages already and I think there is quite some overlap between the 2 (about BN in particular), so there is probably quite a lot of space to gain here if the authors were to reduce a bit the introduction or make the related work a sub-section of the introduction. The ENorm presentation is 4 pages long (which is quite a lot). Section 3.6 might be totally discarded since it is vanilla application of the chain rule. The extension to convolution layers and max pooling could be transferred to the appendix. Minor Comments: You should add “Optimizing neural networks with kronecker-factored approximate curvature” in the literature review about optimization landscape, as it is an important research direction on natural gradient. Conclusion: All in all, I find that this work is a bit too incremental, missing some important comparisons with other techniques and its experimental setup could definitely be improved. Also, the speedup claims should also be supported with empirical experimentation. Revision: I thank the authors for the all the extra experiments they performed. The paper looks good to me, and increased my evaluation accordingly.