Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
Authors: Yulun Zhang
Huan Wang
Can Qin
Yun Fu
Venue: NeurIPS 2021 Spotlight (2021)
Keywords: Neural Network Pruning
Lightweight Image Super-Resolution
Aligned Structured Sparsity Learning

Abstract: Lightweight image super-resolution (SR) networks have obtained promising results with moderate model size. Many SR methods have focused on designing lightweight architectures, which neglect to further reduce the redundancy of network parameters. On the other hand, model compression techniques, like neural architecture search and knowledge distillation, typically consume considerable memory and computation resources. In contrast, network pruning is a cheap and effective model compression technique. However, it is hard to be applied to SR networks directly, because filter pruning for residual blocks is well-known tricky. To address the above issues, we propose aligned structured sparsity learning (ASSL), which introduces a weight normalization layer and applies $L_2$ regularization to the scale parameters for sparsity. To align the pruned locations across different layers, we propose a \emph{sparsity structure alignment} penalty term, which minimizes the norm of soft mask gram matrix. We apply aligned structured sparsity learning strategy to train efficient image SR network, named as ASSLN, with smaller model size and lower computation than state-of-the-art methods. We conduct extensive comparisons with lightweight SR networks. Our ASSLN achieves superior performance gains over recent methods quantitatively and visually.

Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
Yulun Zhanglt
Huan Wang 1t+
Can Qin'
Yun Fu
DDpaarmentt of ECE, Northeastern University
Khoury College of Computer Science, Northeastern University
Abstract
Lightweight image super-resolution (SR) networks have obtained promising sults with moderate model size. Many SR methods have focused on designing lightweight architectures, which neglect further reduce the redundancy of net- work parameters. On the other hand, model compression techniques, like neural architecture search and knowledge distillation, typically consume considerable memory and computation resources. In contrast, network pruning is a cheap and effective model compression technique. However, it is hard to be applied to SR net- works directly, because filter pruning for residual blocks is well-known tricky. To address the above issues, we propose aligned structured sparsity learning (ASSL), which introduces a weight normalization layer and applies L2 regularization to the scale parameters for sparsity. To align the pruned filter locations across different layers, we propose a sparsity structure alignmeni penalty term, which minimizes the norm of soft mask gram matrix. We apply aligned structured sparsity learn- ing strategy to train efficient image SR network. named as ASSLN, with smaller model size and lower computation than state-of-the-ar methods. We conduct ex- tensive comparisons with lightweight SR networks. Our ASSLN achieves superior performance gains over recent methods quantitatively and visually.
Introduction
Image super-resolution (SR) is fundamental computer vision application, which aims recover a high-resolution (HR) image from its resoluttttion (LR) counterpart. In general, image SR is an ill-posed problem, because there exist many HR candidates for one LR input. To alleviate this problem, more and more researchers have been investigating plenty of deep convolutional neural networks (CNNs) [11,3% 31, 38] to achieve more accurate mapping from LR image its HR target.
Deep CNN was firstly introduced for image SR in SRCNN [11] and has attracted continuous attention from both academic and industry communities with its promising SR performance. SRCNN only consists of three convolutional (Conv) layers, hindering its performance. Kim et al. achieved notable improvements over SRCNN by increasing the network depth in VDSR [30] with residual learning. Deeper CNNs could be trained successfully with residual blocks [22]. By utilizing simplified residual blocks, Lim et al. [38] built much deeper network EDSR. Zhang et al. [64] proposed residual channel attention network (RCAN), which is one of the deepest SR networks. With increased network size (i.e., deeper and wider), very deep networks, like EDSR [38] and RCAN [64], have achieved remarkable SR performance. However, they also suffer from some drawbacks such as heavy model parameters, number of operations, and inference time. Therefore, it is impractical to directly deploy them on resource-limited platforms without neural processing units off-chip memory [36].
Equal Contribution
Corresponding author: Huan Wang (wang.huan wvang.huan@northheee @oaaan@@ooottteeemedu
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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IRR (x4)
LapSRN [34] 813K/149.46 4G
MemNet [55] 677K/2,6624C
CARN [1] 1.592K/90.9G
IMDN [27]
EDSR [38]
ASSLN (ours)
Params/FI.OR
715K440.9G
43M/2.99
708K/40.6G
Figure 1: Visual results, parameter number, and FLOPs comparison for 4x SR on Ur [26] dataset Lrng_012 and img_ (20) among lightweight SR networks and a large one EDSR. When calculating FLOPs, we set output size as 3x 1280 720. Our ASSL has the smallest number of parameters and FLOPs, while achieving comparable or even better results than others.
From this point of view, more and more works turn to design lightweight network architectures for efficient image SR [1. 27]. Ahn et al. proposed cascading residual network (CARN) [I] by implementing cascading mechanism upon residual network. Hui et al. proposed information multi- distillation network (IMDN) [27]. Lee et al. introduced knowledge distillation (KD) [25] for image SR [24, 36] with student and teacher networks. Besides, neural architecture search (NAS) [66. 15] was also utilized for lightweight SR models, like MoreMNAS [8] and FAL.RR [7]. However, there still several downsides among these networks: (1) The knowledge distillation based methods usually introduce a large teacher network, which will consume more computation resources during distillation training. (2) The training in some of these NAS-based methods can also consume heavy computation resources. For example, 8 Tesla V100 GPUs are needed to train a single network for about three days in FALSR [7]. (3) Most lightweight SR methods neglect consider the sparsity or redundancy in the Conv kernels, which can be optimized to be more efficient. In short, more effective, resource-friendley and general lightweight SR networks are still in need.
To further peel off the redundancy of Conv kernels, neural network pruning techniques [50, 53] usually introduced reduce the model complexity. Researchers mainly focus on filter pruning (a.k.a. structured pruning, e.g., [37]) rather than weight-element pruning (a.k.a. unstructured pruning, e.g., [18, 17J) for acceleration. Bridging filter pruning with image SR seems plausible solution to strike a better eefformance-compleen
trade-off. However, filter pruning methods in image classification can hardly be transferred to SR networks directly. The main reason is that residual blocks have become a essential component in state-of-the SR networks to ease the training (e.g., the deep version of EDSR [38] has 80 residual blocks; RCAN [64] even has 200 residual blocks). However, it is well- welln that residual connections are hard to prune in structured pruning [37].
To tackle the above issues, present aligned structured sparsity learning (ASSL) for efficient image SR (see Fig. 1). ASSL is essentially a regularization baased filter pruning method. We introduce weight normalization layer [51] after each convolutional layer and apply sparsity-inducing T-2 regularization to the scale parameters in the weight normalization. Besides, central problem in pruning residual networks in image SR is to align the consequent sparsity structure across different layers (see Fig. 2 constrained Conv layers). In this regard, we propose a novel sparsity structure alignment regularization term encourage the pruned filter locations across different layers to be the same. To the best of our knowledge, our ASSL is the first attempt to leverage filter pruning for efficient image SR. The main contributions of our work can be summarized as follows:
We propose aligned structured sparsity learning (ASSL) for efficient image super-resolution (SR). To the best of our knowledge, jointly optimizing image SR networks with structured sparsity constraint has received little research attention so far.
Our ASSL offers a generic framework to structurally prune SR networks with extensive residual connections. To tackle the pruned filter location mismatch issue, sparsity structure alignment penalty term is introduced align the pruned filter indices across different layers.
We employ ASSL to train an efficient aligned structured sparsity learning network (ASSLN), with detailed pruning process visualization for analysis. Our ASSLN achieves superior gains over SOTA lightweight image SR methods quantitatively and visually.
2 Related Work
Deep Image SR Models. Dong el al. [11] firstly introduced CNN with 3 Conv layers for image SR. Residual learning was introduced in VDSR [30], reaching 20 Conv layers. Lim et al. [38]

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Skip Connection
WN
WN
W
F10
FOIT)
Residual Block
Figure 2: Illustration of filter pruning within residual block. Feature maps F are depicted as 3d cubes. Convolutional kernel W (4d tensor) is expended as a 2d matrix (each row stands for filter). Both orange and yellow colors mean the pruned filters: orange represents the pruned filters in free Conv layers; yellow represents the pruned filters in constrained Conv layers. We add an extra weight normalization (WN) layer right after each Conv layer. The main point of ASSL is to apply L2 regularization to the unimportant WN scale parameters for sparsity and regularize the indices of pruned WN scales in the constrained Conv layers to be as close as possible to each other.
proposed EDSR with simplified the residual block [22]. Zhang et al. [64] proposed even deeper network RCAN. Liu et al. proposed FRANet [39] to make the residual features more focused on critical spatial contents. Later, Zhang al. [65] proposed residual non-local attention for image restoration, including image SR. Mei et al. proposed CSNLN [45] by combining feature correlations, and external statistics. Most of them have achieved state-of-the-art results with deeper and wider networks. However, they suffer from huge model sizes and heavy computation operations.
Lightweight Image SR Models. Of late, lightweight image SR models have attracted rising attention. Kim er al. firstly introduced recursive learning in DRCN to decrease model size [31]. Ahn et al. designed a cascading mechanism upon residual network in CARN [1]. Hui et al. proposed lightweight information multi-distillation network (IMDN) [27]. Meanwhile, neural architecture search introduced for image SR in FALSR [7]. Besides, model compression techniques, like knowledge distillation, have been investigated for image SR [24]. Lee et al. trained a teacher network to distill knowledge to a student [36]. Although those lightweight networks have achieved great progress, they still need considerable extra computation resources.
Neural Network Pruning. Pruning removes redundant parameters in neural network without performance seriously compromised [50, 53, 5, 6]. Pruning methods can be mainly grouped into structured pruning (i.e., filter pruning) [37, 60, 23, 58] or unstructured pruming [18, 17]. Structured pruning produces regular sparsity after pruning, beneficial to acceleration. In contrast, unstructured pruning results in irregular sparsity, beneficial compression (i.e., large sparsity) while hard to leverage for actual acceleration [60, 57]. We focus on filter pruming in this work for acceleration.
Most pruning papers focus finding better pruning criterion to select insignificant parameters to remove (50, 53, 5, 6, 4J. There are two paradigms to resolve this problem: regularization-ben based and penalty importance-based. The former selects unimportant weights by adding sparsity term, jointly optimized with the original loss function (e.g., [60, 42]). The latter selects unimportant weights via certain derived mathematical formula (e.g., [35, 20, 18, 17, 37, 47, 48]). Note, there is no strict boundary between the two paradigms. Several works [10, 57, 58] select unimportant weights by some importance criterion and introduce penalty term for sparsity as well. The proposed method in this paper falls into the last category (see Sec. 3.2 for more details).
To our best knowledge, papers before have successfully joined filter pruning with SR for efficient inference with promising results. We will discuss length the difficulties within and bridge the gap.
3 Proposed Method
We first give a brief view of the image SR problem setting by using deep CNN. We also observe that there exists heavy redundancy in the networks. To pursue more efficient image SR networks, we then propose aligned structured sparsity learning (ASSL) to train lightweight model, resulting in ASSLN.
3.1 Deep CNN for Image SR
Given a low-resolution (LR) image LRR, image super-resolution (SR) aims reconstruct its high- resolution (also known as super-resolved) image ISR. Such process can be described as follows,
1sR FSR(IER;
(I)
3

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where FsR(-) is the deep image SR network and @ denotes the network parameters. We also model the LR image In from its HR counterpart as a degradation process
IR = Fu. (IHR):
where (+) downscales the original ground truth IH with scaling factor 8. The downscaling process may introduce additional noise, blurring, compression, and/or other unknown artifacts. Meanwhile, high-frequency information will be lost, more or less. Image SR models try to recover -egeefreyeency information as much as possible. Here, we focus on efficient neural networks with relatively fewer parameters and computation operations, but comparable or even higher performance.
3.2 Aligned Structural Sparsity Learning (ASSL)
In general, our aligned structural sparsity learning method regularization based structured pruning method for efficient SR networks. In the following, we will explain (1) what parameters are regularized obtain sparsity, (2) how select unimportant parameters regularize, (3) which is the specific regularization form, and (4) how to align the sparsity structure for residual networks.
(1) Regularizing Scales in Weight Normalization. The goal of structured pruning is remove filters of a convolutional layer based on some established importance criterion. A natural way is to introduce a gate variable G control the throughput of each filter (e.g., [40, 33, 29], one filter has a gate accordingly) - zeroed gate implies the associated filter contributes nothing to the subsequent layers, thus can be removed. By regularizing the gate variable, we can know which filters are less important than the others. In classification, previous works [38, 64] have shown regularizing the scaling factor in BN [28] natural materialization of this idea. Unfortunately, BN is well-known in practice not useful (even harmful) to SR networks (thus not integrated into state-of-ther ther SR networks [38, 64)). Therefore, the existing solutions cannot carry over to SR networks.
To resolve this issue, we resort to weight normalization [51] (WN), which proposes to decouple the direction learning of filter from its norm learning. Specifically, in WN, each filter is normalized to unit length and an extra learnable scale parameter is used to learn the filter magnitude,
W
W. = - % W., for i (1.2. ........000 N.}
(3)
Will
where W E BNXCCHWW represents the 4d convolutional kernel, and y E R.V. stands for the 1d trainable scale parameters in WN. With weight normalization, we have the y akin to the scale parameters in BN. Then, we can impose certain regularization on 7 to induce sparsity.
(2) Pruning Scheme and Criterion. The next question is how to select unimportant 7 to enforce sparsity (so that we can eventually remove the associated filters). Ideally, we demand a selection mechanism with easy user control. In [41], they sort the BN scales globally (namely, scales from different layers are compared together). For image SR networks, however, this scheme can hardly work. The main reason lies in the architecture difference of image SR networks vs. the mainstream classification networks. Image SR networks (e.g., RCAN [64]) typically have many more residual connections than those (e.g., ResNet101 [21]) in classification. The global sorting scheme cannot guarantee the two layers that are added together keep the same number of filters. To resolve this problem, we turn to adopt a local pruning scheme. For each layer, pre-defined sparsity level is given. Filters in a layer are only compared to each other within that layer.
As for the criterion issue, previous regularization- based pruning methods typically add a sparsity- inducing penalty term (e.g., L1 regularization [41, 611, L2-norm regularization [60]) to the loss. The advantage of this paradigm is that the network can learn to select unimportant filters itself without using a sub-optimal human-defined criterion, yet at a cost there is no established relation between the penalty strength and the desired sparsity. It is very common in practice we need to hard tune the penalty strength hyper-parameter to strike good balance between obtaining desired sparsity and not wver-penalizing the network [60, 57]. On the other hand, previous pruning works [16, 58] in classification have shown that the simple L -norm criterion actually works pretty well in practice. L Li-norm criterion is well-known only crude in terms of characterizing the incurred loss change when a weight is pruned from the network [35, 20J. However, it is rather simple (no extra acquisition cost during SGD training) with easy user control. Its crudity can also be compensated by the plasticity of deep networks [46, 57]. All taken into consideration, we choose Lı -noro as the pruning criterion. Specifically, for 1-th layer, sort the filters by their L Looomm, and set those with the least norms as unimportant filters, denoted as set S()) Then, we apply sparsity- iiduucig regularization to the

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weight normalization scales corresponding to those unimportant filters. Note, we do not enforce any constraint to the important filters since they will stay in the network, no need restrict their learning.
(3) Regularization Form. Here we pin down the sparsity-inducing (SI) regularization form. By conventional wisdom in machine learning I.1 T- regularization may be natural choice for sparsity [13, 33. However, it is hard to control the proper penalty strength by our observation. Instead, we choose to impose L2 regularization on the scale parameters in weight normalization,
Σ 7 1=1 (ESIO
(4)
where a is the scalar loss weight; ?, denotes the i-th element of yy 8()) represents the unimportant filter index set of I-th layer. As inspired by [57, 58], the Lg regularization strength grows gradually (added by a preset constant A every T iterations) during the sparsity learning process, so that the unimportant filters can be compressed to negligible amount. As termination condition, a ceiling limit r (a pre-defined constant) is introduced for the regularization co-efficient a. When for unimportant filters reaches т, the pruning process is finished, followed by finetuning.
The above local pruning scheme can ensure different layers are pruned by the same number of filters. However, it cannot guarantee the pruned locations are (close to) the same. This will cause a problem for pruning residual networks about sparsity structure alignment, as explained next.
(4) Sparsity Structure Alignment. Residual networks [21] are well-known difficult to prune because the add operations (a.k.a. residual/skip connections) in residual blocks demand the pruned filter indices to be the same. Filter pruning via the proposed ASSL method within a residual block is shown in Fig. 2. There
two kinds of convolutional (Conv) layers based their connection relationship with each other. One group comprises the layers that can be pruned without any constraint, which we call free Conv layers in this work; the other consists of layers in which the filters must be pruned at the same indices, called constrained Conv layers. For concrete example, in Fig. 2, the layer (i) is a free Conv layer and layer ((+1) is constrained Conv layer.
Because of the aforementioned sparsity structure constraint issue, many structured pruning algorithms in classification simply do not prune the last Conv layer in a residual blocks [37, 9, 58J. However, this naive solution cannot carry over the image SR networks. The fundamental reason lies in the architecture difference between SR networks and their counterparts in classificatton. First, image SR networks typically employ many more residual blocks. In some top-performed SR networks (e.g.- RCAN [64]), there are even residuals in residuals. Second, each block of SR networks typically has only two Conv layers while ResNets [21] in classification typically have three in block. Third, the residual block of ResNets [21] typically possesses a bottleneck structure, where the unpruned constrained Conv layer is 1 1x 1 Conv, accounting for little FLOPs; while for SR networks, the constrained Conv layers make up an unignorable portion. To see how serious this problem is, taking EDSR as an example, it has 32 residual blocks and each block has two Conv layers. we do not prune the 2nd Conv layer in a residual block, half of the Conv layers are not pruned. In other word, we can only achieve 2x theoretical acceleration at best. The real wall-clock speedup probably is even marginal, seriously hindering its practical application.
Given the issue above, is necessary to prune all the layers in residual blocks if we seek acceleration for practical use. Thus, it is straightforward to find a method to align the pruned indices in all constrained Cony layers. Regularization then is a natural choice considering its wide use in enforcing sparsity structure priors in neural network pruning [50, 60, 58].
Concretely, we propose sparsity struc- ture alignment (SSA) regularization term. For two mask vectors m (t) m)) (for i-th
6
and j-th constrained layer, respectively) in which zero entries suggest which filters are
pruned, if the pruned locations in these two layers are exactly aligned (namely,
mask matrix M
mask matrix M
mask gram matrix
m = mi)), then the inner-produce them, m() mff) maximized (e.g., Row 2 and 8 in Fig. 3). Therefore, we see that the inner-product of masks is good optimization tar- get to align the pruned filter locations. For multiple layers, the mask vectors make up a matrix
Figure 3: Regularizing the gram matrix of scale matrix.
5

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M E N (where N: is the number of constrained layers and Ny is the number of filters in each constrained layer). The inner- inner-products of all combinations make the gram matrix of M, M MM Then the loss term is
K Σ (MM*)s. ).
1
CSSA = K
(5)
where K is the total number of elements in matrix MM.. One problem of this penalty term is that the 0/1-valued hard mask is not differential. To resolve this, we propose to employ Sigmoid function to obtain soft masks. Specifically, given a pre-specified sparsity ratio r, we sort the weight normalization scales y in ascending order and obtain the threshold scale as Yth- Then the soft mask for the i-th weight normalization scale in I-th layer can be formulated as
(6) With these soft masks, MMT become differential and the loss Eq. (5) can be integrated plug- Bolu-and-pun and-play
m (1) Sigmoid( (1)
Yoh -
into original SGD optimization (note this penalty term is only imposed on constrained Conv layers).
In the pruning process, this sparsity structure alignment term is jointly optimized with the sparsity inducing loss (Eq. (4)) for a pre-defined number of iterations (e.g.. we set 1=2.56x 10%). After that, the sparsity structure is well-aligned and we can apply Lı-norm sorting to the scales in weight normalization decide the unimportant filters in constrained Conv layers.
To sum, the pipeline of the proposed algorithm is: (1) For free Conv layers, we apply sparsity- inducing regularization (Eq. (4)) directly; (2) For constrained Conv layers, we apply sparsity-structure alignment regularization (Eq. (5)) for NSsA (a preset constant) epochs and then apply the sparsity- inducing regularization to them. We provide the detailed algorithm in the supplementary material.
3.3
Arm Image SR Models with ASSL
The proposed ASSL approach can be applied as drop-in module to state-of-the-an SR models simply add the two penalty terms (Eqs. (4) and (5)) to the original loss function of an SR method. All the features in the original SR method can stay as they are. The proposed penalty term along with weight normalization layers can be implemented very easily on any cooommtieddifferettattte framework for training deep neural networks. When the pruning process is finished, we remove the unimportant filters, which results in a small model. Then we finetune the small model to regain performance following the common practice [50]. Note weight normalization is only needed in the pruning stage. During finetuning, all the weight normalization layers will be removed.
3.4 Implementation Details
Here we elaborate the details about how to apply ASSI. constructing lightweight image SR models. First, we revise EDSR baseline (i.e., 16 residual blocks) [38] by removing the final Conv layer to reduce parameters. Same as IMDN [27], the image reconstruction is done via the pixel-shuffle layer [52]. We set kernel size as 3 for convolution kernel in all convolutional (Conv) layers. For Conv layers with kernel size xx3 (regardless of channel dimensions), to keep size fixed. We set the initial channel number in the revised EDSR baseline as 256 and then prune to 48. It should be noted the residual scaling factor in each residual block is set as 1. For x2, we compress the parameter number from 19.5M to 692K and the FLOPs from 4,492.5G to 159.1G.
padding strategy used
4 Experimental Results
4.1 Experimental Settings
Data and Evaluation. Following most recent works [56, 38, 63, 19], we use DIV2K [56] and Flickr2K [38] as training data. For testing, we use five standard benchmark datasets: Set5 [2]. Set14 [62], B 100 [43], Urban 100 [26], and Manga109 109 [44]. The SR results are evaluated with PSNR and SSIM [59] on Y channel of transformed YCbCr space. We also provide model size and FLOPSs (a.k.a. Mult-Adds) comparisons. When calculating FLOOPs, we set the output size as 3x 1280x
Training Settings. Following [38, 64], we perform data augmentation the training images, which are randomly rotated by 90", 180 270 and flipped horizontally. Each training batch consists of 16 LR color patches, whose size is 48 48. Our ASSLN model is trained by ADAM optimizer [32] with 31=0.9, 82=0.999, and c=10- We set the initial learning rate as 10 and then decrease it to half every 2x 10t iterations. We use PyTorch [49] to implement models with a Tesla V100 GPU.
"Our code and trained models available at wtt.ghhhw...o//iinnuuuun
www.///nnnnnTT////SSSLLLLLL Tse/ASSL.

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38
37.9937.84
37.85
37.73
37.48
model. mooel.bodyy 88 8.8..yyoyyy.
PNSR (dB)
34
36
34.7
33.67
33,83
33.52
34.23
WN mean scale
1.0
0.4
SI Sl cD co-efficientt a
Pruned filters
32
0.5
Kepe Eltters
0.2
30
L1-norm
28
ASSL (ours)
27.34
0.0
0.0
0.0
0.1
0.3
0.5
0.7
0.9
20
40
60
80
100
Preming ratio
Iteration (k)
Figure 4: Left: PSNR (dB) comparison of models finetuned for only 1 epoch, pruned by L1-norm vs. our ASSL. Right: Illustration of the pruning process of Conv layer "model. body. wwwd....ooooooo.... 8. in EDSR. The WN (weight normalization) mean scale of pruned or kept filters are plotted to the left y-axis (in black); regularization multiplier a is plotted to the right y-axis (in red).
4.2 Ablation Study
For ablation study, we use EDSR baseline (i.e., 16 residual blocks, 64 features) [38] as back- bone, because it is widely used image SR base- line with public code' and results.
Pruning ratio
0.1
0.3
0.5
0.7
0.9
Params (K)
1,10188
681.1
381.8
154.2
26.9
FLOPs (G)
254.5
157.7
88.9
36.5
7.3
Scratch
37.85
37.81
37.75
37.56
36.74
L1-norm [37]
37,91
37.81
37.73
37.58
36.87
Comparison with Baseline Methods. We first conduct ablation study to demonstrate the ef- fectiveness of the proposed ASSL method. We compare two baseline approaches here: train- ing from scratch and the L11 -norm pruning [37] (which simply removes filters with the smallest T Ti-norms and is the most prevailing filter prun- ing method now). The results are presented in Tab. 1. (1) The networks pruned by our method consistently achieve the best PSNR against different pruning ratios. This shows ASSL is not merely effective (outperforming the scratch training), but also more effective than naively applying the existing pruning method in classification to image SR (outperforming -n-morm pruning). (2) Notably, under larger pruning ratio, the ad advantage of ASSL over scratch training and L1 Lı-norm pruning is more evident in general, implying that our approach is more effective in extreme pruning cases. (3) Another point worth mention is that method also adopts the I, L--norm as pruning criterion, the same as [37]. However, our results are significantly better than theirs. This is because their method does not enforce any regularization to the resulted sparsity structure. Thus the remaining feature map channels are actually misaligned in residual blocks of different layers after pruning. Even with a small pruning ratio, the incurred performance damage is very significant, as shown in Fig. 4(Left)- with 0.1 pruning ratio, the network pruned by L -nor degrades PSNR by 3.29 dB, while ours only decreases PSNR by 0.15 dB. It also indicates that our ASSL maintains most representation ability.
ASSL (ours)
37.94
37.91
37.82
37.70
37.23
Gain (ours/scr.)
+0.09
+0.10
+0.07
+0.14
+0.49
Gain (ours/L.)
+0.03
+0.10
+0.09
+0.12
+0.36
Table 1: PSNR (dB) comparison on Set5 (x2)
between ASSL and other two methods to obtain the same small network. The unpruned model is EDSR baseline (Params: 1,369.9K, FLOPs: 316.3G, PSNR: 37.99 dB).
Regularization Visualization. To figuratively understand how ASSL works, in Fig. 4(Right) we plot the regularization multiplier a and the mean scale in weight normalization (WN) layer of EDSR baseline during the ASSL training. The mean scale is split into two parts, pruned and kept. As seen, the regularization multiplier linearly arises against the training epochs as we design. Meanwhile, the mean WN scale of the pruned filters decreases little by little as the penalty becomes stronger. One interesting point is that, note the L -n-omss of the mean scale of the kept filters goes up themselves (no regularization term is employed to encourage them to grow larger). It means the network learns to protect itself from the pruning process, reminiscent of the compensation effect in human brain [14].
4.3 Comparisons with Lightweight SR Networks
We compare our lightweight network ASSLN with representative lightweight SR networks: SRCNN
[11], FSRCNN [12), VDSR [30], DRCN [31], LapSRN [34], DRRN [544], MemNet [55], CARN [1] and IMDN [27]. We show extensive quantitative comparisons in Tabs. 2, 3 and visual ones in Fig. 5.
Performance Comparisons. Tab. 2 shows PSNR/SSIM comparisons for x2, x3, and x4 SR. IMDN [27] ranks the second best except for x4 SR on Mangal09. When compared to all other
7

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Method
Scale
Set5
Set14
B100
Urban
100
Manga T0y
PSNR
SSIM
PSNR
SSIM
PSKR
SSIM
SSIM
PSNR
SSIM
SRCNN
X2
36.66
0.9542
32,42
0.9063
31.36
0.8879
29.50
0.8946
35.60
0.9663
FSRCNN [12]
X2
37.00
0.9558
32.63
0,9088
31.53
0.8920
29.88
0,9030
36.67
0.9710
VDSR [30]
x2
37,53
0.9587
33.03
0.9124
31.90
0.8960
30.76
0,9140
37.22
0.9750
DRCN [31]
X2
37.63
0.9588
33.04
0.91 18
31.85
0.8942
30.75
0.9133
37.63
0.9740
LapSRN [34]
X2
37.52
0.9590
33.08
0.9130
31.80
0.8950
30.41
0.9100
37.27
0.9740
DRRN [54]]
X2
37.74
0.9591
33.23
0.9136
32.05
0.8973
31.23
0.9188
37.92
0,9760
[55]
X2
37.78
0.9597
33,28
0.91442
32.08
0.8978
31.31
0.9195
37.72
0.9740
CARN [1]
X2
37.76
0.9590
33.52
0.9166
32.09
0.8978
31.92
0.9256
38.36
0.9764
IMDN [27]
X2
38.00
0,9605
33.63
0.9177
32.
0.8996
17
0.9283
0.9773
ASSLN (ours)
X2
38.12
0,9608
33,77
0.9194
32.27
0.9007
32.41
0,9309
39.12
0,9781
SRCNN[U]
X3
32.75
0,9090
29.28
0.8209
28.41
0,7863
26.24
0.7989
50,48
0,9117
FSRCNN [12]
X3
33.16
0.9140
29.43
0.8244
28.53
0.7910
26.43
0,8080
31.10
0,9210
VDSR [30]
x3
33.66
0.9213
29.77
0.8314
28.82
0.7976
27.14 14
0.8279
32.01
0,9340
DRCN [1]
X3
33.82
0.9226
29.76
0.8311
28.80
0.7963
27.15
0.8276
32.31
0.9360
DRRN [S4]
X3
34,03
0.9244
29, 29.96
0.8349
28.95
0.8014
27.53
0.8378
32.74
0.9390
MemNet [55]
X3
34.09
0.9248
30.00
0,8350
0.8001
27.56
0.8376
32.51
0.9369
CARN [1]
X3
34.29
0.9255
30,29
0.8407
29.06
0.8034
28.06
0,8493
33,50
0.9539
IMDN [27]
X3
34,36
0.9270
30.32
0.8417
29. 09
0.8046
28.17
0.8519
33.61
0,9444
ASSLN (ours)
X3
34.51
0,9280
30.45
0,8439
29.19
0,8069
28.35
0,8562
34,00
0,94168
SRCNNLLI
X4
30.48
0,8628
27.49
0.7503
26.90
0.7101
24.52
0.7221
27. 27.58
0.8355
FSRCNN [12]
X4
30.71
0.8657
27.59
0.7535
26.98
0.7150
24.62
0,7280
27.90
0,8610
VDSR [30]]
X
31.35
0.8838
28.01
0.7674
27.29
0.7251
25.18
0.7524
28. 83
0.8870
DRCN [3]]
X4
31.53
28.02
0.7670
27.23
0.7233
0.7510
0.8870
LapSRN [34]
X44
31.54
0,8850
28.
0.7720
27.32
0.7280
25.21
0,7560
29. .9
0.8900
DRRN [54]
X4
31.68
0.8888
28.21
0.7720
27.38
0.7284
25. 44
0,7638
29
0.8960
MemNet [55]
X4
31.74
0.8893
28.26
0.7723
27.40
0.7281
25.50
0,7630
29.42
0,8942
CARN 131
X-4
32.13
0.8937
28.60
0.7806
58
0.7349
26.07
0.7837
30.46
0,9083
IMDN [27]
X44
32.21
0.8948
28.58
0.7811
27.56
0.7353
26.04
0,7838
30.45
0,9075
ASSLN (ours)
X4
32.29
0.8964
28.69
0.7844
27.66
0.7384
26.27
0,7907
30.84
0.9119
Table 2: PSNR/SSIM comparisons. Best and second best results are colored with red and blue.
Method
x3
x4
Params
Mult-Adds
Params
Mult-Adds
Params
Muli-Adds
SRCNN [1]]
57K
52.7G
57K
52.7G
52.7G
FSRCNN [12]
12K
6.0G
12K
5.0G
12K
4.6G
VDSR [30]
665K
612.6G
612.6G
665K
612.6G
DRCN [3]]
1,774K
17,974.3G
1,774K
17,974.3G
1,774K
17,974,3G
LapSRN [34]
813K
29.9G
N/A
N/A
813K
149.4G
DRRN [54]
297K
6.796.9G
297K
6,796.90
297K
6.796.90
MemNet [S5]
677K
2,662.4G
677K
2,662.4G
677K
2,662.4G
CARN
1.592K
222.80
1,592K
1,592K
90.90
IMDN [27]
694K
158.8G
703K
71.5G
715K
40.9G
ASSLN (ours)
692K
159.1G
698K
71.2G
708K
40.6G
Table 3: Model size and Mult-Adds comparisons of lightweight SR networks with different scales.
methods, our ASSLN performs the best on all the datasets across all scaling factors. Specifically, let's take the challenging x4 SR as an example. Our ASSLN obtains about 0.23 dB on Urban 100 and 0.38 dB on Manga109 009 PSNR gains over the second best method, respectively. These comparisons show the effectiveness of ASSLN, which learns the aligned structured sparsity. Different from careful network designs as most compared methods have done, we start with the existing EDSR baseline [38] and prune it to a much smaller network. We make better use of the internal sparsity of the network and increase the efficiency of the learned network parameters.
Model Size and Mult-Adds. Tab. 3 provides parameter number and Multi-Adds comparison with different scales. Although some previous lightweight SR models (c.g., SRCNN and FSRCNN) cost very small number of parameters and FLOPs, they also have limited performance. Compared with recent popular works (e.g., DRRN, MemNet, CARN, and IMDN), our ASSLN has the least parameter number. We also provide operations number with Mult-Adds. Our ASSLN operates least Mult-Adds than most compared methods except for the FLOPs for x2. When we consider Tabs. 2, 3 together, we find that our ASSLN achieves better trade-off between performance and resource consumption. Those comparisons indicate that ASSLN reduces parameters and operations efficiently.
o
Visual Comparisons. We further provide visual comparisons (x4) in Fig. 5 for challenging cases. For example, in img_m0000 008, we can observe that most of the compared methods cannot recover structural details with proper directions and/or suffer from blurring artifacts. In contrast, our ASSLN can better alleviate the blurring artifacts and recover more structural details. Similar observations can be found in other cases. These visual comparisons are consistent with the quantitative results, demonstrating the superiority of our method. Our ASSLN learns the aligned structured sparsity from a large network and prunes it to a much smaller one, but still maintains most representation ability.
8

---

HQ
Bicubic
SRCNN (11]
FSRCNN (12)
VDSR [30]
Urban100: ing OUS (x4)
LapSKN [34]
MemNet [55]
CARN
IMDN [27]
ASSLN (ours)
HQ
Bicubic
SRCNN [11]
FSRCNN [12)
VDSR [30]
Urban 100: ing. 058 (x4)
LapSRN [34]
MemNet [55]
CARN (1]
IMDN [27]
ASSLN (ours)
HQ
Bicubic
SRCNN [11]
ESRCNN (12]
VDSR [30]
Urhan 100: img. 073 (x4)
LapSRN [34]
MemNet [55]
CARN [1]
TMDN [27]
ASSI. N (ours)
HQ
Bicubic
SRCNN [1]]
ESRCNN [12]
VDSR [30]
Urbun100: imy 089 (x4)
LapSRN [34]
MemNet [55]
CARN []]
IMDN [27]
ASSLN (ours)
Figure 5: Visual comparison ( (x4) with lightweight SR networks on Urban100 dataset.
4.4 Comparisons with Other Model Compression Methods
To further show the effectiveness of our network pruning method, we compare our ASSLN with representative model compression techniques for image SR. Specifically, we compare with neural architecture search (NAS) based meth- ods (i.e., MoreMNAS-A [8] and FALSR-A (7]) and knowledge distillation (KD) based methods (i.e., CARN+KD [36]). We provide quantitative results Tab, 4. The results of compared methods are copied from their papers directly. Our ASSLN obtains the best performance with the least parameter number and Mult-Adds. With our aligned structured sparsity learning strategy, we do have search lots of architectures or train teacher network, which usually consume plenty of extra computation resources.
Method
Params
Mult-Adds
Set5
BT00
MoreMNNAS
1,039K
238.601
37.63
31.95
FALSR-A [7]
1,021K
234.7G
37.82
32.12
CARN+KD [36]
1,592K
222.8G
37.82
32.08
ASSLN (ours)
692K
159.1G
38.12
32.27
Table 4: Model size, Mult-Adds, and PSNR com- parisons (x2) among model compression methods.
5 Conclusion
Lately, researchers have been investigating lightweight image super-resolution (SR) networks and achieving promising results with moderate model size and FLOPs. Meanwhile, model compression techniques, like neural architecture search and knowledge distillation, have also been introduced for efficient SR network design. However, they usually consume expensive computation resources. Network pruning is another popular model compression technique, but it is hard to train lightweight SR networks directly because of extensive residual connections in SR. To address these issues, we propose aligned structured sparsity learning (ASSL), which introduces weight normalization layer and imposes La regularization to the scale parameters for sparsity. We further propose sparsity structure alignment penalty term to align the locations across different layers. We employ such an aligned structured sparsity to train efficient image SR network (ASSLN), Our ASSIN achieves superior performance over recent state-of-ther -ht-t-rrt methods quantitatively and qualitatively.
Acknowledgments. This research is supported by the U.S. Army Research Office Award I9I

---

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12


Reviews:
[{"review_id": "ukXbw0A67jM", "reviewer": "Reviewer_k8sL", "summary": "The paper proposes a novel aligned structured sparsity learning (ASSL) method to prune the SR network. To tackle the pruned filter location mismatch issue in SR networks, a sparsity structure alignment penalty term is introduced to align the pruned filter indices across different layers. The final pruned network achieves SOTA performance among lightweight SR methods.", "questions": "", "limitations": "", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper is well-written and easy to read.\n\nThe proposed ASSL properly addresses the problems (including lack of  BN layers and location mismatch) encountered in pruning SR networks. The details are well clarified and the experiments are adequate to demonstrate the effectiveness of ASSL.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "1. It seems that the SSA regularization term can only encourage m_i = m_j, and is not a strict constraint. However, to my understand, only when m_i is exactly the same as m_j, the corresponding residual blocks can be pruned.\n\n2. In Sec 4.4, it is better to add a comparison with EDSR + KD, since ASSLN is also developed over EDSR.\n\n3. It is better to add comparisons on run-time.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "1 hours"}, "extra_text": {}}, {"review_id": "VmGHZaNaq4i", "reviewer": "Reviewer_1kmf", "summary": "This paper proposes a lightweight super-resolution network. A  filter pruning method and similarity-based sparsity structure alignment module are employed in this paper. Experimental and visual results on different SR datasets prove the performance of the proposed method.", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "- Originality\n\nThe Novelty of this article is limited. The filter pruning method is widely used in previous lightweight networks. The idea of “the sparsity or redundancy in the Conv kernels” is out of date [1,2,3]. \n\n- Quality\n\nThe NAS-related method is time-consuming, and this is widely recognized. In lines 43-44, the authors show that some SR methods are time-consuming and need heavy computation resources through NAS[7].  This is inappropriate and unfair for most SR methods. The NAS-related method is not the mainstream of SR.\n\nIn the section of Sparsity Structure Alignment, the similarity-related method is a common idea in other methods, e.g. image transfer, KD, transfer learning. The authors should show their improvement.\n\nThe ablation analysis is not enough. The author needs to do the Basic module ablation analysis.\n\n The authors should compare their method with SOTA lightweight SR method [4,5,6] in Table 2. They should also discuss the Lightweight SR method with the normal model，and show the gap between the lightweight method and other methods.\n\nReferences:\n\n[1] Exploiting kernel sparsity and entropy for interpretable CNN compression, CVPR 2019\n\n[2] Sparse convolutional neural networks, CVPR 2015\n\n[3] An information theoretic approach of designing sparse kernel adaptive filters, IEEE Transactions on Neural Networks 2009\n\n[4] Madnet: A fast and lightweight network for single-image super resolution, IEEE Transactions on Cybernetics 2020\n\n[5] LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block, ECCV 2020\n\n[6] s-LWSR: Super Lightweight Super-Resolution Network,  IEEE Transactions on Image Processing 2020\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "See the main review.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "5 hours"}, "extra_text": {}}, {"review_id": "GoAbytLZfet", "reviewer": "Reviewer_XysC", "summary": "To address the lightweight image super-resolution (SR), this paper proposes an aligned structured sparsity learning (ASSL) by introducing a weight normalization layer and L2 regularization. Besides, the authors propose a sparsity structure alignment penalty term to align the pruned locations across different layers. To train an efficient image SR network, they apply an aligned structured sparsity learning strategy (ASSLN) with small model size and low computation.  Extensive experiments demonstrate the effectiveness of the proposed method. ", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "1. Some motivations should be highlighted. This paper states that “it is hard to be applied to SR networks directly, because filter pruning for residual blocks is well-known tricky”. Some image SR methods (e.g., IPT) do not use residual blocks. Therefore, it may not be the main reason that the network pruning is hard to be applied to SR.\n\n2. The novelty of the proposed method should be highlighted.  The proposed method seems to combine the network pruning method and the image SR method. Actually, applying the network pruning into many tasks is difficult, not limited to the image SR task. The authors should highlight that such a combination is non-trivial. \n\n3. Some technical details are not clear. In Line 146, why the global scheme cannot work for image SR networks? Why the main reason lies in the architecture difference between the image SR networks and classification networks. However, some classification networks also use residual blocks. \n\n4. The experiments can be improved. Why not compare with RCAN? Why not apply the proposed method to RCAN? Is the proposed method limited to the number of residual blocks? In Table 2, it would be better to report the results of EDSR. In addition, it would be better to compare with another state-of-the-art image SR method (IPT).\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes", "review_text": "", "extra_scores": {"ethical_concerns": "No", "needs_ethics_review": "No", "ethics_review_area": ["I don’t know"], "time_spent_reviewing": "5 hours"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper studies the issue of pruning for super-resolution networks.  The reviewers agree that the approach to pruning architecture-specific components, like resnet blocks, offers some interesting advances.  However the authors seem to agree that the paper could do a better job communicating the novelty of the proposed methods, and that experiments show fairly small improvements over existing methods for pruning networks of this type.


Decision: Accept (Spotlight)