# Fast Flow Reconstruction via Robust Invertible n × n Convolution

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## Abstract

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## 1. Introduction

**Contributions:**This work generalizes the invertible $1\times 1$ convolution to an invertible $n\times n$ convolution by reformulating the convolution layer using our proposed invertible shift function. Our contributions can be summarized as follows:

- Firstly, by analyzing the standard convolution layer, we reformulate its equation into a form such that, rather than shifting the kernels during the convolution process, shifting the input provides equivalent results.
- Secondly, we propose a novel invertible shift function that mathematically helps to reduce the computational cost of the standard convolution while keeping the range of the receptive fields. The determinant of the Jacobian matrix produced by this shift function can be computed efficiently.
- Thirdly, evaluations of several datasets on both objects and faces have shown the generalization of the proposed $n\times n$ convolution using our proposed novel invertible shift function.

## 2. Related Work

**invertible**and

**tractable**transformations. Unlike GAN, the model explicitly learns the data distribution $p\left(\mathbf{x}\right)$ and therefore the loss function is efficiently employed with the log-likelihood.

**Coupling Layers:**NICE [31] and RealNVP [32] presented coupling layers with a normalizing flow by stacking a sequence of invertible bijective transformation functions. The bijective function is designed as an affine coupling layer, which is a tractable form of Jacobian determinant. RealNVP can work in a multi-scale architecture to build a more efficient model for large inputs. To further improve the propagation step, the authors applied batch normalization and weight normalization during training. Later, Ho et. al. [43] presented a continuous mixture cumulative distribution function to improve the density modeling of coupling layers. In addition to improving the expressiveness of transformations of coupling layers [43], utilized multi-head self-attention layers [44] in the transformations.

**Inverse Autoregressive Convolution:**Germain et al. [45] introduced autoregressive autoencoders by constructing an extension of a non-variational autoencoder that can estimate distributions and is straightforward in computing their Jacobian determinant. Masked autoregressive flow [36] is a type of normalizing flow, where the transformation layer is built as an autoregressive neural network. Inverse autoregressive flow [30] formulates the conditional probability of the target variable as an autoregressive model.

**Invertible $1\times 1$ Convolution:**Kingma et al. [29] proposed simplifying the architecture via invertible $1\times 1$ convolutions. Learning a permutation matrix is a discrete optimization that is not amenable to gradient ascent. However, the permutation operation is simply a special case of a linear transformation with a square matrix. We can pursue this work with convolutional neural networks, as permuting the channels is equivalent to a $1\times 1$ convolution operation with an equal number of input and output channels. Therefore, the authors replace the fixed permutation with learned $1\times 1$ convolution operations.

**Activation Normalization:**[29] performs an affine transformation using scale and bias parameters per channel. This layer simply shifts and scales the activations with data-dependent initialization that normalizes the activations given an initial minibatch of data. This allows the scaling down of the minibatch size to 1 (for large images) and the scaling up of the size of the model.

**Invertible $\mathit{n}\times \mathit{n}$ Convolution:**Since the invertible $1\times 1$ convolution is not flexible, Hoogeboom et al. [35] proposed an invertible $n\times n$ convolution generalized from the $1\phantom{\rule{3.33333pt}{0ex}}\times \phantom{\rule{3.33333pt}{0ex}}1$ convolutions. The authors presented two methods to produce the invertible convolutions: (1) Emerging Convolution and (2) Invertible Periodic Convolutions. Emerging Convolution is obtained by chaining specific invertible autoregressive convolutions [30] and speeding up this layer through the use of an accelerated parallel inversion module implemented in Cython. Invertible Periodic Convolutions transform data to the frequency domain via Fourier transform; this alternative convolution has a tractable Jacobian determinant and inverse. However, these invertible $n\times n$ convolutions require more parameters; therefore, these have an additional computational cost compared to our proposed method.

**Lipschitz Constant:**Behrmann et al. [37] developed a theory that any residual blocks satisfying the Lipschitz Constant can be invertible. Hence, Behrmann et al. proposed an invertible residual network (i-ResNet) as a normalizing flow-based model. Similar to [29,31,32,35], i-ResNet is learned by optimizing the negative log-likelihood in which the inverse flow and Jacobian determinant of the residual block can be efficiently approximated by the stochastic methods. Inheriting the success of Lipschitz theory, Kim et al. [38] proposed an ${L}_{2}$ self-attention that allows the self-attention of the Transformer networks [44] to be invertible.

## 3. Background

#### 3.1. Flow-Based Generative Model

#### 3.2. Standard $n\times n$ Convolution

## 4. Invertible $\mathbf{n}\times \mathbf{n}$ Convolution

#### 4.1. Invertible Shift Function

#### 4.2. Invertible $n\times n$ Convolution

Algorithm 1: Invertible $n\times n$ Convolution |

Input: An input $\mathbf{X}\in {\mathbb{R}}^{N\times H\times W\times C}$ |

Result: An output of invertible $n\times n$ convolution and the log Jacobian determinant |

Initialize $\alpha ,\beta \in {\mathbb{R}}^{C}$ for the invertible shift function; |

Initialize $\mathbf{W}\in {\mathbb{R}}^{C\times C}$ as a rotation matrix for the invertible $1\times 1$ convolution function; |

logdet = 0.0; |

The invertible shift function; |

$\mathbf{Y}=\mathbf{X}\times \alpha +\beta $ (Channel-wise operations); |

The inverse will be $\mathbf{X}=\frac{\mathbf{Y}-\beta}{\alpha}$; |

logdet = logdet + ${\sum}_{i=1}^{C}log\left({\alpha}_{i}\right)$; |

The invertible$1\times 1$convolution; |

$\mathbf{Z}=Conv(\mathbf{Y},\mathbf{W})$; |

The inverse will be $\mathbf{Y}=Conv(\mathbf{Z},{\mathbf{W}}^{-1}$); |

logdet = logdet + $log(det(\mathbf{W}\left)\right)$ * H * W; |

Return $\mathbf{Z}$ and logdet; |

## 5. Experiments

#### 5.1. Quantitative Experiments

**Datasets and Metric:**We evaluate our invertible $n\times n$ convolution on CIFAR-10 (Figure 4a) and ImageNet (Figure 4b) with $32\times 32$ and $64\times 64$ image sizes. We use bits per dimension as the criteria with which to evaluate models. We compare our method against RealNVP [32], Glow [29] and Emerging Convolution [35]. We adopt the network structures of Glow and replace all invertible $1\times 1$ convolutions of Glow with our invertible $n\times n$ convolutions. For the data preprocessing, we follow the same process as in RealNVP [32].

**Network Configurations:**In the CIFAR experiment, the depth of flow K and the number of levels L are set to 32 and 3, respectively. Meanwhile, the depth of flow in ImageNet experiments is set to 48, the numbers of levels of ImageNet $32\times 32$ and ImageNet $64\times 64$ experiments are set to 3 and 4, respectively. We use the Adam optimizer [47] to optimize the networks in which batch size and learning rate are set to 64 (per GPU) and $0.001$, respectively. We choose Normal Distribution as the prior distribution ${p}_{\mathcal{Z}}\left(z\right)\sim \mathcal{N}(\mathbf{z};0,\mathbf{I})$ in all experiments.

**Results:**Table 2 shows our experimental results. In particular, our proposal helps to improve the generative models on ImageNet $32\times 32$ and ImageNet $64\times 64$ datasets, which are more challenging than CIFAR-10. In particular, our proposed method achieves a state-of-the-art performance in which the bit per dimension results in ImageNet $32\times 32$, and ImageNet $64\times 64$ is

**3.96**and

**3.74**, respectively. In comparison, the Emerging Convolution [35] and Glow achieve similar results in both ImageNet $32\times 32$ and ImageNet $64\times 64$ benchmarks, which are 4.09 and 3.81, respectively. Meanwhile, the corresponding results of RealNVP on these benchmarks are 4.28 and 3.98, respectively. As shown by the results, our proposed invertible $n\times n$ convolution provides a better generative capability than the stand-alone invertible $1\times 1$ convolution. Since Emerging Convolution uses invertible auto-regressive convolution, our proposal is, therefore, less complicated and has faster inference than Emerging Convolution. In the CIFAR-10 benchmark, although our model does not perform as well as Glow [29] and Emerging Convolution [35], we find it interesting that our method gains competitive results with a small number of modifications. The gap in performance is partially caused by the small amount of CIFAR-10 data that is inefficient for training the well-generalized convolution.

#### 5.2. Qualitative Experiments

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Reformulating $n\times n$ convolution. We propose to shift inputs instead of kernels. The proposed invertible $n\times n$ convolution will be simplified as a combination of the invertible shift function $\mathcal{S}$ and the invertible $1\times 1$ convolution.

**Figure 3.**(

**a**) is our one step of flow using an invertible $n\times n$ convolution. Our proposal flow step is able to combine with the multi-scale architecture designed in RealNVP (

**b**). K and L are the depth of flow and the number of levels, respectively.

Description | Function | Reverse Function | Log-Determinant |
---|---|---|---|

ActNorm [29] | $\mathbf{y}=\mathbf{x}\odot \gamma +\beta $ | $\mathbf{x}=(\mathbf{y}-\beta )/\gamma $ | $\sum log\left|\gamma \right|$ |

Affine Coupling [32] | $\mathbf{x}=[{\mathbf{x}}_{a},{\mathbf{x}}_{b}]$ | $\mathbf{y}=[{\mathbf{y}}_{a},{\mathbf{y}}_{b}]$ | $\sum log\left|s\right({\mathbf{x}}_{b}\left)\right|$ |

${\mathbf{y}}_{a}={\mathbf{x}}_{a}\odot s\left({\mathbf{x}}_{b}\right)+t\left({\mathbf{x}}_{b}\right)$ | ${\mathbf{x}}_{a}=[{\mathbf{y}}_{a}-t\left({\mathbf{y}}_{b}\right)]/s\left({\mathbf{y}}_{b}\right)$ | ||

$\mathbf{y}=\left[{\mathbf{y}}_{a}{\mathbf{x}}_{b}\right]$ | $\mathbf{x}=\left[{\mathbf{x}}_{a}{\mathbf{y}}_{b}\right]$ | ||

$1\times 1$ conv [29] | ${\mathbf{y}}_{:,i,j}=\mathbf{W}{\mathbf{x}}_{:,i,j}$ | ${\mathbf{x}}_{:,i,j}={\mathbf{W}}^{-1}{\mathbf{y}}_{:,i,j}$ | $h.w.log|det\mathbf{W}|$ |

Our Shift Function | ${\mathbf{y}}_{c,i,j}={\alpha}_{c}{\mathbf{x}}_{c,i,j}+{\beta}_{c}$ | ${\mathbf{x}}_{c,i,j}=[{\mathbf{y}}_{c,i,j}-{\beta}_{c}]/{\alpha}_{c}$ | $h.w.{\sum}_{c}log\left|{\alpha}_{c}\right|$ |

**Table 2.**Comparative results (bits per dimension) of proposed invertible $n\times n$ convolution compared to RealNVP, Glow and Emerging Convolution.

Models | CIFAR-10 | ImageNet 32 | ImageNet 64 |
---|---|---|---|

RealNVP | 3.49 | 4.28 | 3.98 |

Glow | 3.35 | 4.09 | 3.81 |

Emerging Conv | 3.34 | 4.09 | 3.81 |

Ours | 3.50 | 3.96 | 3.74 |

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## Share and Cite

**MDPI and ACS Style**

Truong, T.-D.; Duong, C.N.; Tran, M.-T.; Le, N.; Luu, K. Fast Flow Reconstruction via Robust Invertible *n* × *n* Convolution. *Future Internet* **2021**, *13*, 179.
https://doi.org/10.3390/fi13070179

**AMA Style**

Truong T-D, Duong CN, Tran M-T, Le N, Luu K. Fast Flow Reconstruction via Robust Invertible *n* × *n* Convolution. *Future Internet*. 2021; 13(7):179.
https://doi.org/10.3390/fi13070179

**Chicago/Turabian Style**

Truong, Thanh-Dat, Chi Nhan Duong, Minh-Triet Tran, Ngan Le, and Khoa Luu. 2021. "Fast Flow Reconstruction via Robust Invertible *n* × *n* Convolution" *Future Internet* 13, no. 7: 179.
https://doi.org/10.3390/fi13070179