S ALIENCY M IX : A S ALIENCY G UIDED D ATA A UGMENTATION S TRATEGY FOR B ETTER R EGULARIZATION
Anonymous authors
Paper under double-blind review
A BSTRACT
Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models.
Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regularization.
However, such information removal is undesirable.
On the other hand, recent strategies suggest to randomly cut and mix patches and their labels among training images, to enjoy the advantages of regional dropout without having any pointless pixel in the augmented images.
We argue that such random selection strategies of the patches may not necessarily represent sufﬁcient information about the corresponding object and thereby mixing the labels according to that uninformative patch enables the model to learn unexpected feature representation.
Therefore, we propose SaliencyMix that carefully selects a representative image patch with the help of a saliency map and mixes this indicative patch with the target image, thus leading the model to learn more appropriate feature representation.
SaliencyMix achieves a new state-of-the-art top-1 error of 20 . 09% for ResNet-101 architecture on ImageNet classiﬁcation and also improves the model robustness against adversarial perturbations.
Furthermore, models that are trained with SaliencyMix help to improve the object detection performance.
I NTRODUCTION
Machine learning has achieved state-of-the-art (SOTA) performance in almost every ﬁeld, especially in computer vision tasks.
This success can mainly be attributed to the deep architecture of convolutional neural networks (CNN) that typically have 10 to 100 millions of learnable parameters.
Such a huge number of parameters enable the deep CNNs to solve extremely complex problems.
However, besides the powerful representation ability, a huge number of parameters increase the probability of overﬁtting when the number of training examples is insufﬁcient, which results in a poor generalization capability of the model.
In order to improve the generalization ability of deep learning models, several data augmentation strategies have been studied.
Random feature removal is one of the popular techniques that guides the CNNs not to focus on some small regions of input images or on a small set of internal activations thereby improving the model robustness.
Dropout (Nitish et al., 2014; Tompson et al., 2015) and regional dropout (Junsuk & Hyunjung, 2019; Terrance & Graham, 2017; Golnaz et al., 2018; Singh & Lee, 2017; Zhun et al., 2017) are two established training strategies where the former randomly turns off some internal activations and later removes and/or alters random regions of the input images.
Both of them force a model to learn the entire object region rather than focusing on the most important features and thereby improving the generalizability of the model.
Although dropout and regional dropout improve the classiﬁcation performance, this kind of feature removal is undesired to the CNN since they discard a notable portion of informative pixels from the training images.
Recently, Yun et al.
(2019) proposed CutMix, that randomly replaces an image region with a patch from another training image and mixes their labels according to the ratio of mixed pixels.
This method does not allow the training images to have any uninformative pixels while enjoying the properties of regional dropout.
However, we argue that the random selection process may have
T a r g e t I m a g e S
o u
r c e
I m a g e
Dog - 80% ?
Cat - 20% ?
Augmented Image
Augmented Label Dog - 80% ?
Cat - 20% ?
Label Original Image
Dog Cat T a r g e t
I m a g e S
o u
r c e
I m a g e
Dog - 80% ?
Cat - 20% ?
Augmented Image
Augmented Label Dog - 80% ?
Cat - 20% ?
Label Original Image
Dog Cat T a r g e t
I m a g e S ou r
ce
I m a g e
Dog - 80% ?
Cat - 20% ?
Augmented Image
Augmented Label Dog - 80% ?
Cat - 20% ?
Label Original Image
Dog Cat
[Figure]
some possibility to select a patch from the background region irrelevant to the target objects in the source image, by which an augmented image may not contain any information about the corresponding object as shown in Figure 1.
The selected source patch (background) is highlighted with a black rectangle on the source image.
Two possible augmented images are shown wherein both of the cases, there is no information about the source object (cat) in the augmented images despite their mixing location on the target image.
However, their interpolated labels encourage the model to learn both objects’ features (dog and cat) from that training image.
But we recognize that it is undesirable and misleads the CNN to learn unexpected feature representation.
Because, CNNs are highly sensitive to textures (Geirhos et al., 2019) and since the interpolated label indicates the selected background patch as the source object, it may encourage the classiﬁer to learn the background as the representative feature for the source object class.
We address the aforementioned problem by carefully selecting the source image patch with the help of some prior information.
Speciﬁcally, we ﬁrst extract a saliency map of the source image that highlights semantically important regions and then select a patch surrounding the peak salient region of the source image to assure that we select from the object part and then mix it with the target image.
Now the selected patch contains relevant information about the source object that leads the model to learn more appropriate feature representation.
This more effective data augmentation strategy is what we call, ” SaliencyMix ”.
We present extensive experiments on various standard CNN architectures, benchmark datasets, and multiple tasks, to evaluate the proposed method.
In summary, SaliencyMix has obtained the new SOTA top-1 error of 2 .
76% and 16 .
56% for WideResNet (Zagoruyko & Komodakis, 2016) on CIFAR-10 and CIFAR-100 (Krizhevsky, 2012), respectively.
Also, on ImageNet (Olga et al., 2015) classiﬁcation problem, SaliencyMix has achieved the new SOTA top-1 and top-5 error of 21 .
26% and 5 .
76% for ResNet-50 and 20 .
09% and 5 .
15% for ResNet-101 (He et al., 2016).
In object detection task, initializing the Faster RCNN (Shaoqing et al., 2015) with SaliencyMix trained model and then ﬁne-tuning the detector has improved the detection performance on Pascal VOC (Everingham et al., 2010) dataset by +1 .
77 mean average precision (mAP).
Moreover, SaliencyMix trained model has proved to be more robust against adversarial attack and improves the top-1 accuracy by 1 .
96% on adversarially perturbed ImageNet validation set.
All of these results clearly indicate the effectiveness of the proposed SaliencyMix data augmentation strategy to enhance the model performance and robustness.
R ELATED W ORKS 2. D ATA A UGMENTATION
The success of deep learning models can be accredited to the volume and diversity of data.
But collecting labeled data is a cumbersome and time-consuming task.
As a result, data augmentation strategy comes into account that aims to increase the diversity of existing data by applying various transformations.
Since this simple and inexpensive technique signiﬁcantly improves the model performance and robustness, data augmentation has widely been used to train deep learning models.
Lecun et al.
(1998) applied data augmentation to train LeNet for hand written character recognition.
They performed several afﬁne transformations such as translation (horizontal and vertical), scaling, shearing, etc.
For the same task, Bengio et al.
(2011) used a deeper network and in addition to the afﬁne transformation, they applied more diverse transformation such as Gaussian noise, salt and pepper noise, Gaussian smoothing, motion blur, local elastic deformation, and various occlusions to the images.
Krizhevsky et al.
(2012) applied random image patch cropping, horizontal ﬂipping and randomly changing the color intensity using principal component analysis (PCA) based color augmentation.
In Deep Image (Wu et al., 2015), color casting, vignetting, and lens distortion are applied besides ﬂipping and cropping to improve the robustness of a very deep network.
Besides these manually designed data augmentations, Lemley et al.
(2017) proposed an end-to-end learnable augmentation process, called Smart Augmentation.
They used two different networks where one is used to learn the suitable augmentation type and the other one is used to train the actual task.
Devries & Taylor (2017) proposed Cutout that randomly removes square regions of the input training images to improve the robustness of the model.
Zhang et al.
(2017) proposed MixUp that blends two training images to some degree where the labels of the augmented image are assigned by the linear interpolation of those two images.
But the augmented images look unnatural and locally ambiguous.
Recently, Yun et al.
(2019) proposed CutMix that randomly cuts and mixes image patches among training samples and mixes their labels proportionally to the size of those patches.
However, due to the randomness in the source patch selection process, it may select a region that does not contain any informative pixel about the source object, and the label mixing according to those uninformative patches misleads the classiﬁer to learn unexpected feature representation.
In this work, the careful selection of the source patch always helps to contain some information about the source object and thereby solves the class probability assignment problem and helps to improve the model performance and robustness.
2. L ABEL S MOOTHING
In object classiﬁcation, the class labels are usually represented by one-hot code i.e., the true labels are expected to have the probability of exactly 1 while the others to have exactly 0.
In other words, it suggests the model to be overconﬁdent.
However, the CNN models employ the softmax function that is unable to predict a class with an exact probability of 1 or 0.
As a result, the models start learning ever-larger weight values that make the model less adaptive and too conﬁdent about its predictions.
Label smoothing allows to relax the model conﬁdence on the labels by setting the class probabilities to a slightly shifted intermediate values.
Thereby, helps the model to be more adaptive instead of being over-conﬁdent and ultimately improves the model robustness and performance (Szegedy et al., 2016).
Our method also mixes the class labels and enjoys the beneﬁt of label smoothing.
2. S ALIENCY D ETECTION
The human visual systems (HVS) contain a natural attention mechanism that automatically focuses on the most important points in any visual scene.
In computer vision, the visual saliency detection models aim to simulate this HVS attention mechanism (for more details, please refer to Cong et al.
Hou & Zhang (2007) proposed a spectral residual method that focuses on the properties of background.
Speciﬁcally, this method ﬁrst analyzes the log-spectrum of an image and then extracts the spectral residual in spectral domain and ﬁnally reconstructs the saliency map in the spatial domain.
Achanta et al.
(2009) proposed a frequency tuned approach that preserves the boundary information by retaining a sufﬁcient amount of high-frequency contents.
Wang & Dudek (2014) considered the saliency detection as a background subtraction problem and proposed a fast pixellevel adaptive background detection algorithm.
This method records per-pixel historical background values and their importance based on the occurrence statistics that help to remove the least useful information and enables to detect the salient region with high accuracy.
Qin et al.
(2019) exploited the power of deep CNN and proposed a boundary-aware saliency detection network (BASNet) that consists of a densely supervised Encoder-Decoder network.
In this paper, since our goal is to restrict the source patch selection to the objects of interest, we have used the saliency detection proposed by Wang & Dudek (2014) that highlights the objects rather than the backgrounds.
Section 3.3 further explains the effects of different saliency detection algorithm on the proposed data augmentation method.
[Figure]
Detection
Peak Salient
[Figure]
Saliency
[Figure]
P ROPOSED M ETHOD
Similar to Yun et al.
(2019), we cut a source patch and mix it to the target image and also mix their labels proportionally to the size of the mixed patches.
But in order to prevent the model from learning any irrelevant feature representation, the proposed method enforces to select a source patch in a way so that it must contains information about the source object.
It ﬁrst extracts a saliency map of the source image to highlight the objects of interest and then selects a patch surrounding the peak salient region to mix with the target image.
Here we explain the process in detail.
3. S ELECTION OF THE S OURCE P ATCH
The goal of saliency detection is to ﬁnd out the pixels or regions that are attractive to the HVS and to assign them with higher intensity values (Cong et al., 2019).
A saliency detection method produces the visual saliency map, a gray-scale image, that highlights the objects of interest and thereby mostly focuses on the foreground.
Let I s ∈ R W × H × C is a randomly selected training image with label y s from which a patch will be cut.
Then its saliency map detection can be represented as
[Equation]
where I vs ∈ R W × H represents the saliency map of the given source image I s as shown in Figurewhere the objects of interest have higher intensity values and f ( · ) represents a saliency detection model.
Then we search for a pixel I i,jvs in the saliency map that has the maximum intensity value.
The i, j represent the x and y coordinates of the most salient pixel and can be found as
[Equation]
Then we select a patch, either by centering on the I i,jvs − th pixel if possible, or keeping the I i,jvs − th pixel on the selected patch.
It ensures that the patch is selected from the object region, not from the background.
The size of the patch is determined based on a combination ratio λ which is sampled from the uniform distribution (0 , 1) to decide the percentage of an image to be cropped.
3. M IXING THE P ATCHES AND L ABELS
Let I t ∈ R W × H × C is another randomly selected training image with label y t , to where the source patch will be mixed.
SaliencyMix partially mixes I t
and I s to produce a new training sample I a , the augmented image, with label y a .
The mixing of two images can be deﬁned as
[Equation]
where I a denotes the augmented image, M ∈ { 0 , 1 } W × H represents a binary mask, M (cid:48) is the complement of M and (cid:12) represents element-wise multiplication.
First, the source patch location is deﬁned by using the peak salient information and the value of λ and then the corresponding location of the mask M is set to 1 and others to 0 .
The element-wise multiplication of M with the source image results with an image that removes everything except the region decided to keep.
In contrast,
M (cid:48) does the opposite of M i.e., the element-wise multiplication of M (cid:48) with the target image keeps all the regions except the selected patch.
Finally, the addition of those two creates a new training
[Table]
T a r g e t P a t c h M
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S a li e n t t o S a li e n t S a li e n t t o N on
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S a li e n
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S l a i e n c y D e t e c t i o n M e t
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T u n i n g B a c k g r o u n
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t r a c t i
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n S p e c t r
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ti
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a nd T a r g e t P a t c h M
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[Figure]
sample that contains the target image with the selected source patch in it (See Figure 2).
Besides mixing the images we also mix their labels based on the size of the mixed patches as
[Equation] [Equation]
where y a denotes the label for the augmented sample and λ is the combination ratio.
Other ways of mixing are investigated in Section 3.4.
3. I MPACT OF D IFFERENT S ALIENCY D ETECTION M ETHODS
Here we investigate the effect of incorporating various saliency detection methods in our SaliencyMix data augmentation.
We use four well-recognized saliency detection algorithms (Wang &
Dudek, 2014; Hou & Zhang, 2007; Achanta et al., 2009; Qin et al., 2019), and perform experiments using ResNet-18 and ResNet-50 as baseline models on CIFAR-10 and Tiny-ImageNet datasets for 200 epochs and 100 epochs, respectively.
Figure 3(a-b) show that the fast self-tuning background subtraction proposed by Wang & Dudek (2014) performs better on both the datasets and the effects are identical on CIFA10 and ImageNet datasets.
Because this method aims to highlight the foreground over the background and well suits our intention to ﬁnd out the object of interest.
3. D IFFERENT W AYS OF S ELECTING AND M IXING THE S OURCE P ATCH
There are several ways to select the source patch and mix it with the target image.
In this section, we explore those possible schemes and examine their effect on the proposed method.
We use
ResNet-18 and ResNet-50 architectures with SaliencyMix data augmentation and perform experiments on CIFAR-10 and Tiny-ImageNet datasets, respectively.
We found out ﬁve possible schemes: ( i ) Salient to Corresponding , that selects the source patch from the most salient region and mix it on the corresponding location of the target image; ( ii ) Salient to Salient , that selects the source patch from the most salient region and mix it on the salient region of the target image; ( iii ) Salient to NonSalient , that selects the source patch from the most salient region but mix it on the non-salient region of the target image; ( iv ) Non-Salient to Salient , that selects the source patch from the non-salient region of the source image but mix it on the salient region of the target image; and ( v ) Non-Salient to Non-Salient , that selects the source patch from the non-salient region of the source image and also mix it on the non-salient region of the target image.
To ﬁnd out the non-salient region, we use the least important pixel of an image.
Figure 3(c-d) show the classiﬁcation performance of the proposed SaliencyMix data augmentation with the above mentioned selection and mixing schemes.
Similar to the Section 3.3, the effects of the proposed method are identical for CIFAR10 and Tiny-ImageNet datasets.
Both the Non-Salient to Salient and Non-Salient to Non-Salient select the source patch from the non-salient region of the source image that doesn’t contain any information about the source object and thereby produce large classiﬁcation error compared to the other three options where the patch is selected from the most salient region of the source image.
It justiﬁes our SaliencyMix i.e., the source patch should be selected in such a way so that it must contain information about the source object.
On the other hand, Salient to Salient covers the most signiﬁcant part of the target image that restricts the model from learning its most important feature and Salient to Non-Salient may not occlude the target object
[Table]
which is necessary to improve the regularization.
But Salient to Corresponding keeps balance by changeably occluding the most important part and other based on the orientation of the source and target object.
Consequently, it produces more variety of augmented data and thereby achieves the lowest classiﬁcation error.
Also, it introduces less computational burden since only the source image saliency detection is required.
Therefore, the proposed method uses Salient to Corresponding as the default selection and mixing scheme.
E XPERIMENTS
We verify the effectiveness of the proposed SaliencyMix data augmentation strategy on multiple tasks.
We evaluate our method on image classiﬁcation by applying it on several benchmark image recognition datasets using popular SOTA architectures.
We also use the SaliencyMix trained model and ﬁne-tune it for object detection task to verify its usefulness in enhancing the detection performance.
Furthermore, we validate the robustness of the proposed method against adversarial attacks.
All experiments were performed on PyTorch platform with four NVIDIA GeForce RTX 2080 Ti
GPUs.
4. I MAGE C LASSIFICATION 4.1. CIFAR-10 AND CIFAR-
There are 60 , 000 color images of size 32 × 32 pixels in both the CIFAR-10 and CIFAR-100 datasets (Krizhevsky, 2012) where CIFAR-10 has 10 distinct classes and CIFAR-100 has 100 classes.
The number of training and test images in each dataset is 50 , 000 and 10 , 000 , respectively.
We apply several standard architectures: a deep residual network (He et al., 2016) with a depth of 18 (ResNet18) and 50 (ResNet-50), and a wide residual network (Zagoruyko & Komodakis, 2016) with a depth of 28 , a widening factor of 10 , and dropout with a drop probability of p = 0 .
3 in the convolutional layers (WideResNet-28-10).
We train the networks for 200 epochs with a batch size of 256 using stochastic gradient descent (SGD), Nesterov momentum of 0 . 9 , and weight decay of 5 e − 4 .
The initial learning rate was 0 . 1 and decreased by a factor of 0 .
2 after each of the 60 , 120 , andepochs.
The images are normalized using per-channel mean and standard deviation.
We perform experiments with and without a traditional data augmentation scheme where the traditional data augmentation includes zero-padding, random cropping, and horizontal ﬂipping.
Table 1 presents the experimental results on CIFAR datasets.
The results are reported on ﬁve and three runs average for CIFAR10 and Tiny-ImageNet, respectively.
It can be seen that for each of the architectures, the proposed SaliencyMix data augmentation strategy outperforms all other methods.
It achieves the new SOTA top-1 error of 2 .
76% and 16 .
56% for WideResNet-28-10 on CIFAR10 and CIFAR-100 datasets, respectively.
Moreover, SaliencyMix shows signiﬁcant performance improvement over CutMix (Yun et al., 2019) when applied without any traditional augmentation technique.
It reduces the error rate by 1 . 85% , 2 . 35% , and 1 .
14% on CIFAR-10 dataset when applied with ResNet-18, ResNet-50 and WideResNet-28-10 architectures, respectively.
Using the same architectures, it reduces the error rate by 5 .
76% , and 3 .
76% on CIFAR-100 dataset, respectively.
[Table] [Table]
[Table]
4.1. I MAGE N ET
ImageNet (Olga et al., 2015) contains 1 . 2 million training images and 50 , 000 validation images of 1000 classes.
To perform experiments on ImageNet dataset, we apply the same settings as used in
Yun et al.
for a fair comparison. We have trained our SaliencyMix for 300 epochs with an initial learning rate of 0 . 1 and decayed by a factor of 0 . 1 at epochs 75 , 150 , and 225 , with a batch size of 256 .
Also, the traditional data augmentations such as resizing, cropping, ﬂipping, and jitters have been applied during the training process.
Table 2 presents the ImageNet experimental results where the best performance of each method is reported.
SaliencyMix outperforms all other methods in comparison.
It drops top-1 error for ResNet-50 by 1 . 66% , 1 . 31% , and 0 .
14% over Cutout, Mixup and CutMix data augmentation, respectively.
For ResNet-101 architecture, SaliencyMix achieves the new best result of 20 . 09% top-1 error and 5 .
15% top-5 error.
4. O BJECT D ETECTION USING P RE -T RAINED S ALIENCY M IX
In this section, we use the SaliencyMix trained model to initialize the Faster RCNN (Shaoqing et al., 2015) that uses ResNet-50 as a backbone network and examine its effect on object detection task.
The model is ﬁne-tuned on Pascal VOC 2007 and 2012 datasets and evaluated on VOC 2007 test data using the mAP metric.
We follow the ﬁne-tuning strategy of the original method (Shaoqing et al., 2015).
The batch size, learning rate, and training iterations are set to 8 , 4 e − 3 , and 41 K and learning rate is decayed by a factor of 0 .
1 at 33 K iterations.
The results are shown in Table 3.
Pretraining with CutMix and SaliencyMix signiﬁcantly improves the performance of Faster RCNN.
Because in object detection, foreground information (positive data) is much more important than the background (Lin et al., 2017).
Since SaliencyMix helps the augmented image to have more foreground or object part than the background, it leads to better detection performance.
It can be seen that SaliencyMix trained model outperforms other methods and achieves a performance gain of +1 .
77 mAP.
4. C LASS A CTIVATION M AP (CAM) A NALYSIS
Class Activation Map (CAM) (Zhou et al., 2016) ﬁnds out the regions of input image where the model focuses to recognize an object.
Here we compare the CAM output of the proposed SaliencyMix with other data augmentation techniques.
We train each data augmentation method by using a vanilla ResNet-50 model on ImageNet to extract CAM.
Figure 4 shows the visual comparison of
CAM output on two mixed classes, ’Golden Retriever’ and ’Tiger Cat’.
It can be seen that Mixup has a severe problem of being confused when trying to recognize an object because the pixels are mixed and it is not possible to extract class speciﬁc features.
Also, Cutout suffers disadvantages due to the uninformative image region.
On the other hand, both the CutMix and SaliencyMix effectively focuses on the corresponding features and precisely localizes the two objects in the scene.
4. R OBUSTNESS A GAINST A DVERSARIAL A TTACK
Deep learning based models are vulnerable to adversarial examples i.e., they can be fooled by slightly modiﬁed examples even when the added perturbations are small and unrecognizable
[Figure]
[Figure] [Figure]
(Szegedy et al., 2014; Goodfellow et al., 2015; Madry et al., 2017).
Data augmentation helps to increase the robustness against adversarial perturbations since it introduces many unseen image samples during the training (Madry et al., 2017).
Here we verify the adversarial robustness of a model that is trained using various data augmentation techniques and compare their effectiveness.
Fast
Gradient Sign Method (FGSM) (Madry et al., 2017) is used to generate the adversarial examples.
ImageNet pre-trained models of each data augmentation techniques with ResNet-50 architecture is used in this experiment.
Table 4 reports top-1 accuracy of various augmentation techniques on adversarially attacked ImageNet validation set.
Due to the appropriate feature representation learning and focusing on the overall object rather than a small part, SaliencyMix signiﬁcantly improves the robustness against the adversarial attack and achieves 1 .
96% performance improvement over the nearly comparable method CutMix (Yun et al., 2019).
4. C OMPUTATIONAL C OMPLEXITY
We investigate the computational complexity of the proposed method and compare it with other data augmentation techniques in terms of training time.
All the models are trained on CIFAR-10 dataset using ResNet-18 architecture for 200 epochs.
Table 5 presents the training time comparison.
It can be seen that SaliencyMix requires a slightly longer training time compared to others, due to saliency map generation.
But considering the performance improvement, it can be negligible.
C ONCLUSION
We have introduced an effective data augmentation strategy, called SaliencyMix, that is carefully designed for training CNNs to improve their classiﬁcation performance and generalization ability.
The proposed SaliencyMix guides the models to focus on the overall object regions rather than a small region of input images and also prevents the model from learning in-appropriate feature representation by carefully selecting the representative source patch.
It introduces a little computational burden due to saliency detection, while signiﬁcantly boosts up the model performance and strengthen the model robustness on various computer vision tasks.
Applying SaliencyMix with WideResNet achieves the new SOTA image classiﬁcation top-1 error of 2 .
76% and 16 .
56% on CIFAR-10 and CIFAR-100, respectively.
On ImageNet classiﬁcation, applying SaliencyMix with ResNet-50 and ResNetobtains the new SOTA top-1 error of 21 .
26% and 20 .
09% , respectively.
On object detection, using the SaliencyMix trained model to initialize the Faster RCNN (ResNet-50 as a backbone network) and ﬁne-tuning leads to a performance improvement by +1 .
77 mAP.
Furthermore, SaliencyMix trained model is found to be more robust against adversarial attacks and achieves 1 .
96% accuracy improvement on adversarially perturbed ImageNet validation set compared to the nearly comparable augmentation method.