Learning Debiased and Disentangled Representations for Semantic Segmentation
Authors: Sanghyeok Chu
Dongwan Kim
Bohyung Han
Venue: NeurIPS 2021 Poster (2021)
Keywords: Debiasing
Disentanglement
Semantic Segmentation

Abstract: Deep neural networks are susceptible to learn biased models with entangled feature representations, which may lead to subpar performances on various downstream tasks. This is particularly true for under-represented classes, where a lack of diversity in the data exacerbates the tendency. This limitation has been addressed mostly in classification tasks, but there is little study on additional challenges that may appear in more complex dense prediction problems including semantic segmentation. To this end, we propose a model-agnostic and stochastic training scheme for semantic segmentation, which facilitates the learning of debiased and disentangled representations. For each class, we first extract class-specific information from the highly entangled feature map. Then, information related to a randomly sampled class is suppressed by a feature selection process in the feature space. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes, and the model is able to learn more debiased and disentangled feature representations. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks, with especially notable performance gains on under-represented classes.

Learning Debiased and Disentangled Representations for Semantic Segmentation
Sanghyeok Chu
Dongwan Kim
Bohyung Han
ECE & ASRI, Seoul National University (sanghyeok.chu chu, dongwan 123, bhhan] Ssnu. ac.kr
Abstract
Deep neural networks susceptible to learn biased models with entangled feature representations, which may lead to subpar performances on various downstream tasks. This particularly sity in the data exacerbates the tendency. This limitation has been addressed mostly in classification tasks, but there little study on additional challenges that may appear in more complex dense prediction problems including semantic segmenta- tion. To this end, we propose a madees-ggnostic and stochastic training scheme for semantic segmentation, which facilitates the learning of debiased and disentangled representations. For each class, we first extract class-specific information from the highly entangled feature map. Then, information related to a randomly sampled class is suppressed by a feature selection process the feature space. By randomly eliminating certain class information in each training iteration, we effectively re- duce feature dependencies among classes, and the model is able to learn more debiased and disentangled feature representations. Models trained with our ap- proach demonstrate strong results on multiple semantic segmentation benchmarks, with especially notable performance gains under-reprees redre-repre classes.
for under-represented classes, where lack of diver-
Introduction
Semantic segmentation is pixelwise classification task that is applicable to wide range of practical problems including autonomous driving, medical image diagnosis, scene understanding. and many others. Ever since deep neural networks have been adopted for semantic segmentation [TS)]. its accuracy has increased gradually with the introduction of stronger network architectures, improved training schemes, and large-scale datasets.
Despite such phenomenal achievement, semantic segmentation approaches still suffer from the chronic limitations caused by class imbalance and stereotyped scene context in datasets. The class imbalance issues are prevalent in semantic segmentation, where certain classes occupy larger area and/or appear more frequently than others. For example, the "road" class in the Cityscapes dataset [6] accounts for over 36% of pixels all training images while the "motorcycle" class does only 11% of pixels. Furthermore, the scene context is not sufficiently diversified in training datasets. For instance, the objects the "motorcycle" class is always observed the vicinity the "road" class, and the spatial layout of the classes almost identical in all images. Consequently. the models often learn substantially correlated representations and fail to accurately delincate the boundaries in the absence of frequently co-located objects.
These issues cause the model to learn suboptimal feature representations, especially for classes that under-represented. These feature representations are often entangled with one another, causing confusion between classes, and are also susceptible to encode adverse biases as well, which may be detrimental to the model's overall performance. On the other hand, for classes with abundant labels, the model is able to leverage the large variations in the data and learn more robust feature
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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representations that are less dependent on the presence of other classes. As such, the model exhibits strong performance on classes with sufficient labels but weaker performances on rare classes.
We propose DropClass, a simple yet effective training scheme for semantic segmentation that aims to alleviate the vulnerability of dataset bias and feature entanglement. Given an input example, we first extract a class-specific feature map for each class using Grad-CAM [Z]. Then, we drop the feature map corresponding a randomly sampled class and aggregate the remaining feature maps to generate the predictions. Finally, we employ our proposed loss functions to facilitate training. This procedure somewhat similar to Dropout [8]. but is specifically designed with a different goal in mind; while Dropout aims to reduce co-adaptations among features, DropClass aims to disentangle the class representations and reduce the underlying biases that arise from inter-class relationships. Our training scheme requires no additional data or annotations. More importantly, since DropClass is model agnostic, can be plugged into any existing network architecture straightforwardly.
Our contributions can be summarized as follows:
Our work takes the first step to tackling dataset biases in the semantic segmentation task. We describe how dataset biases in semantic segmentation are different from those in classifi- cation, and devise a benchmark to better analyze the biases found in segmentation models.
We propose DropClass, a model-agnostic training scheme that results learning more debiased and disentangled feature representations for semantic segmentation.
Our experimental results show that the model trained with DropClass exhibits stronger performance compared to the baseline across multiple network architectures and datasets. Furthermore, we observe significant performance gains especially on under-represented classes, and provide analysis that further validate the effectiveness our training scheme.
2 Proposed Method
We first define the semantic segmentation task and its relevant notations. For a dataset with a set of classes, C, we sample an input image æ E hxxxxx and its corresponding label y E xxx [C], The feature extractor g(-) takes x as input and produces an intermediate feature representation A E A 1 x where is denotes an arbitrary channel size of the feature map that depends on the choice of network architecture. The feature map A is then fed into the classifier, /t()), to produce ý E RR which serves as the final output of the model before the softmax operation, i.e., the logits. Altogether, we denote the entire model as a composition of the feature extractor and classifier: ú h(A) h(g())). Note that throughout the paper, we subscripts refer to specific spatial and channel locations on tensors.
2.1 Motivation
Dataset bias issues are introduced in a plethora of literature dealing with classification problems [+] 13), where the primary concerns are class imbalance in the dataset and spurious correlation between attributes in images. Although the dense prediction tasks including semantic segmentation may suffer from additional challenges related to dataset bias, their potential limitations have still been hardly discussed. Hence, this work first points out critical drawback exposed to semantic segmentation algorithms caused by the stereotyped co-occurrence of multiple classes as well as the issues inherent from the classification tasks.
We argue that, besides the class imbalance and attribute correlation issues, inter-class relationships with spatial co-occurrence or feature similarity could be the culprit of dataset biases in semantic segmentation models. Consider the class relationship between the "car" and "road" classes in road scenes segmentation. The two classes have a strong spatial correlation since the vast majority of "car" samples will be spatially located above the "road" class. Models can easily exploit this spatial correlation in the process of maximizing the pixel-level accuracy. However, this could have adverse effects when the model encounters an image where "cars" do not appear above the "road" class. Moreover, the representations of a class are often entangled with one another since the features of co-occurring classes in an image are mixed in the convolution layers, and this tendency is aggravated with the semantic similarity between classes, e.g., "bicycle + motorcycle" and "person < rider". In both cases, under-represented, rare classes typically become the victims of the feature entanglement.

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1[ *
mee
Vdrop
A'
1[ #
miy
A²
I[2 #
[C]]
A
siei
[C]
Adrop
Conv
Hada mard Product
Indicator
Class Drop Madule
Gradient Flow
Figure 1: An illustration of the DropClass procedure. We first generate importance scores S scc,, which are used to extract class-specific feature maps A . ..?...... A We then drop the class-specific feature map corresponding random class 2, and average all other feature maps. Finally, Adop is fed into the classifier to produce the output without information of class 2.
In this sense, dataset bias and feature entanglement are not mutually exclusive, but dataset bias is manifested as feature entanglement in segmentation tasks.
The fundamental goal of our work is to develop training scheme that allows the model to learn debiased and disentangled representations for each class, such that the model can make robust predictions for the under-represented classes without depending on the feature representations of other classes. In essence, this is achieved by removing the class-specific features of a random class at each iteration and providing a proper loss as described in Section 2.3 Our training process not only breaks up inter-class relationships through stochastic training scheme, but also exposes the classifier to a diverse set of class-specifee feature combinations given the same input.
2.2 DrapClass for Debiasing and Disentangling
The feature representations of multiple classes should not be entangled with one another in order to facilitate less confusion and stronger discriminability. To achieve this, we remove the feature dependencies among classes by selectively discarding the information related to randomly chosen class at each training iteration.
The difficulty is that neural networks are black-box in nature and produce highly entangled feature representations. To alleviate this issue, we leverage Grad-CAM 7 a well-known gradient-based method of highlighting important class-specific information from entangled feature representations. Grad-CAM uses the hackpropagated gradients to produce a coarse localization map that highlights important regions in the image with respect to certain label, and is widely used to visualize class- discriminative features in deep neural networks. similar fashion, we employ the gradients of feature Ai.j. with respect the class logit ÿf as the degree of importance that Aij. has on the prediction for class c. Since semantic segmentation is pixel-level classification task, we average the gradients of Ai,. with respect to every pixel location that corresponds to class This is used as the importance score of Ai,. for class c, and is denoted by S
Dūn,..
S
h X W
0Ai,j
The importance score for a certain class can be calculated for all locations (i.j, k)) of the feature map A to obtain an importance score map S with the same dimensions. Then, we perform element-wise multiplication of the feature and importance score maps to obtain class-specific feature representations, A which is given by
A ReLU(A O St),
(2)
where @ denotes the Hadamard product. The ReLU activation is used to preserve all features of A that have positive influence on je and filter out all negative ones.
With the highlighted feature representation A Ve E C, we are now able to discard information related to certain class from the output of the feature extractor. In every iteration of the training

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process, class 2 is randomly sampled as
(C,)
where (1, (CI) denotes a discrete uniform distribution across elements of C. Through the stochastic drop of the class we deactivate A and obtain a different combination of feature representations at each iteration, which is given by
C Σ
1 [C] 1|c
Admnp
+ 2] A
(4)
where 1[:] represents an indicator function. After passing Alaoop through convolutional layer to compensate missing information, we obtain contains information for all classes except class 2. This feature is passed through the classifier to generate new logit, Dioop- Since Atrp may still contain the information about the dropped class our approach attempts suppress the information regarding class in the other class-specific feature maps as well, using the objective function as defined in Section 2.3 Overall, our DropClass procedure is illustrated in Figure
feature representation Adrop (onn((A'nug) that
2.3 Objective Function
We train the model with multiple loss functions, each with a specific goal in mind. We first evaluate the cross-entropy loss:
C
CCE
yu.v.e log(Pu.v.c),
(5)
h u=lv=l
where أ softmax(j) applied over the channel dimension. To account for the dropped class we design modified cross- cross-entropy loss that masks the pixcl-wise loss at all locations, where class is the ground-truth.
C
LCF drop
Yarnee log(Pn,,.)) 1Byu,,, * 1],
(6)
h
X
W
where ý = softmax (jámp) applied over the channel dimension. Then, LCE for the model's original output and LCF_drop drop for the DropClass output are combined by a weighted sum:
Leeg = (1 A) LCr + À LCE Len_drop: drop:
where X is scaling constant that balances the two loss terms.
The scaling constant plays important role in ensuring the stability and effectiveness of training. The process outlined in Section 2.2 is based on the assumption that the gradient of Ai. with respect ~" accurately quantifies the importance of Aij,k on the prediction of class c. In the initial stages of training, however, ý is likely to be largely inaccurate, and thus, the gradient may not accurately reflect the relationship between the feature and class prediction. Hence, relying on this gradient to perform DropClass from the early stages of training may lead to an unstable training process. To alleviate this issue, we train the model primarily with LeE at the early stages of training and linearly decrease its weight as training progresses. Conversely, we gradually increase the weight for CCE, drop- thereby facilitating learning of debiased and disentangled representations towards the end of training. This achieved by linearly increasing the value of À from 0 to across the duration of training. To further stabilize training, we linearly increase the probability of dropping out any class from 0 to well.
Finally, we add an additional objective term Leup that helps suppress the output probability of the dropped class z:
h
Sup
Pu.0,
(8)
X
Even after removing information about class some information could still persist in the dropped feature Admp, which would allow the model to predict score for class z, albeit with low confi- dence. Hence, by suppressing the probability for class directly, the model is encouraged to learn

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Algorithm Training scheme for DropClass.
Require: Dataset D {(x(i), v()), of classes feature extractor g(+) and classifier ht())
I: for i= 1,2, do
2: Compute
g((())) 1 ((A).
3: for c 2. do
4:
Compute ii hxw
1 Ex=1 E
Gi... aA.
Eq.
5:
Compute A ReLU 5*). 6: end for
Eq.
7: Sample a class z to drop and deactivate A=.
>> Eq.
8: Gather all A for Vc C { and feed them into the classifier h(-).
Eq.
9: Compute loss function and update parameters.
> Eq.
10: end for
class-specific representations, i.e., purge any redundant information related class from feature representations of all other classes C' {z}. Note that this loss is only calculated when class is dropped. Altogether, the final objective function proposed for DropClass is:
Lotal
Leeg + aupup
where is a Slyper-paramesd that weighs the relative importance of Cup. A summary of our proposed training scheme can found Algorithm
Experiments
To evaluate the effectiveness of DropClass, experiment well-known semantic segmentation dataset: Cityscapes [6] with a few reasons. First, it is relatively small, with 2975 train images. Second, it has large class imbalances, with the pixel frequency ranging from 0.1% to 36.9%. These two commonalities make it difficult for an ordinary model to learn robust, debiased representations. We also conduct experiments the Pascal VOC dataset [14], and the results be found in the supplementary document.
3.1 Implementation Details
Network architectures We employ two recent segmentation network architectures, HRNet [] and
D3 (2) with MobileNetV3 [15] backbone, to highlight the model-agnostic property of our method. For HRNet,
the code provided by the author!" and use two different versions of
HRNet: HRNetVI-W18 and HRNetV2-W WI8. For DecpLabV3, we use the official Py PyTcc [16] code") and change the default backbone to MobileNetV3. All backbones are trained the ImageNet [17]
dataset.
Computational overhead Calculating gradients for each class imposes a heavy computational burden, which may lead to slow training. However, we take advantage of simple trick that allows us to calculate the gradient in Eq. (T) efficiently. Since convolution layers are linear operations. of conveniently reduces to the weight of the convolution kernel. Thus, we employ x 1 convolution as the final classification layer, h, which is a common design choice in most semantic segmentation models. Using this trick reduces both the computational and memory overhead of the gradient calculation, and allows it to be processed in parallel as well.
Hyperparameters We compare our model with baseline, where the model is trained with an ordinary cross-entropy loss (Eq. (5)) only. We use the same set of hyperparameters for both experi- ments to ensure fair comparison. The value of the loss weighing term a in Eq. (9) is set 10 for all experiments, which is based on the scale the two loss terms. As mentioned in Section 2.3 the value of À is initialized on gradient computations to generate disentangled features, takes more iterations for the model to fully converge under the DropClass training scheme. We sample a new class for the entire batch at
and scaled linearly up I until the end of training. Since DropClass depends
deeplabv3.py

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Table 1: Categorical IoU scores for the Cityscapes dataset. Classes are sorted in order of increasing pixel frequency. mloUT indicates mloU for the 9 classes with lowest pixel frequency. DeepLab uses MobileNetV3 architecture the backbone.
motereyou
vegetation
rider!
Llight
bicycle
Lagg
parson
pole
sidwwak
building
road
mlo
mloU?
Model
Pixel %
0.1
0.1
0.2
0.2
0.2
0.3
0.4
0.6
0.7
0.9
1.2
1.2
4.0
6.1
7.0
15,9
22.8 36.9
Baseline
47.9
53.5
63.3
47.3
71.8
48.6
72.8
72.8
44.7
52.0
61.5
77.0
56.7
93.5
81.6
92,9
91.5
90.5
97.5
69.3
58.
Ours
50.2
55.7
65.3
48.0
73.4
48.6
74.1
75.3
47.1
53.7
61.9
78.7
61.6
94.0
82.3
93.3
92.1
91.1
97.6
70,7
59.7
HRNaVV
A
2.3
2.2
2.0
0.7
1.6
0.0
1.3
2.5
2.4
1.7
0.4
1.7
4.9
0.5
0.7
0.4
0.6
0.6
0.1
1.4
1.6
A (%)
4.8 4.1
32
1.5
2.2
0.0
1.8
3.4
544
3.3
0.7
2.2
8.6
0.5
2.0
2.9
Bascline
58.3 61.5
69.3
62.7
80.6
68.2
76.3
76.3
50.3
59.5
75.1
67.1
59.7 60.9
71.7
69.7
83.2
68.5
76.8
79.1
55.5
76.6
69.5
HRNetV2
1.4 -0.6
24
7.0
2.4
A (%)
2.4 -1.
3.5
3.2
0.7
3.7
2.0 3.8
Baseline
45.9 51.6
53.7
52.3
66.1
52.3
68.8
66.0
46.0
52.9
57.5
73.1
48.9
92.6
79.5
92.1
90.4
89.7
97.4
67.2
55.9
DeepLabV33
Ours
49.7 53.1
53.7
55.0
67.4
51.9
68.4
66.8
47.1
52.4
58.0
73.5
48.5
92.8
79.1
92.3
90.3
89.9
97.3
67.7
57.0
A
3.8
1.5
0.0
2.7
1.3
-0.4
-04
0.8
111
-0.
0.5
0.4
-04
0.2
0.4
0.2
4.11
0.2
-0.1
0.5
1.1
A (%6
8.3
2.9
0.0
5.2
2.0
-0.8
-0.6
12
2.4
-0.9
0.9
0.5
-08
0.2
0.5
0.2
4.11
0.2
-0.1
0.7
2.3
each training iteration. The rest our hyperparameters are organized as table in the supplementary materials, where we detail the number of iterations, batch size, learning rate, learning rate decay, image size, and type of GPU used for each of our experiments.
3.2 Quantitative and Qualitative Results
Tables presents the categorical Intersection- ceneriioon--e---nnnoon (IoU) and mean-IoU (mloU) scores on the Cityscapes dataset. The absolute change in IoU (A), well the relative change in IoU (A(%)) are reported, to compare our method with the baseline. For better interpretability of our results, the classes are sorted in order of increasing pixel frequency, and also caleulate separate mloU score for the least frequent 50% of classes, which denoted by mloU?.
Our method consistently outperforms the baseline across all models, with 2.0%, 2.0%, and 0.7% gains on the mIoU for HRNetV1, HRNetV2, and DeepLabY V3 models, respectively. More importantly, we observe even larger improvements on the mIoU for the 9 most under-represented classes, which ac- count for merely 2.8% of all pixels: 2.9% on HRNetV1, 1, 3.8% on HRNetV2, and 2.3% on DeepLab V3. These empirical results supports our claim that DropClass helps the model learn debiased feature representations that benefit the model's 's overall performance, especially for under-represented classes. There are few more interesting details observe from Table First, the IoU improves most classes, albeit with smaller margins on well-represented classes. Also, we notice especially large improvements across all models the "train" and "wall" categories, which also happen be two of the least frequently appearing categories: "train" is the least frequent class, appearing in only 0.4% of all images, and "wall" is the fifth least frequent class, appearing only 2.8% of all images. Figure illlustrates the qualitative results of the proposed model in comparison to the baseline on the Cityscapes dataset.
3.3 Comparisons with Other Debiasing Techniques
Many works on debiasing focus on the image classification task, and are not easily applicable the semantic segmentation task. To emphasize that DropClass is better suited for semantic segmentation, we provide comparisons with two baseline debiasing techniques: reweighting and resampling.
Reweighting In Table the results of both the baseline and our model employ median frequency balancing, which is widely used for the Cityscapes dataset. In median frequency balancing, the cross-entropy term reweighted according to the frequency classes. We train the model without this reweighting scheme on both the baseline and the model trained with DropClass. As shown in Table 2 even without the reweighting scheme, the model trained with DropClass outperforms the baseline model with the reweighting scheme.

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(a) GT
(b) Baseline
(c) Ours
Figure 2: Visualization segmentation results on Citycapes. Due to the semantic similarity or spatial co-occurrence between classes, the baseline model misclassifies the least frequent class "motorcycle" as "bicycle" (top) or "person" " (bottom), while the model trained with DropClass predicts correctly for both cases. The white boxes in the images denote the regions with notable differences between the two models.
Table Comparison with the reweighting base- lines on HRNe(V1.
Table 3:
Comparison with "motorcycle" resam- pling on HRNetV1 without reweighting.
mloU
mloU*
loU (motor.) mloU
Baseline w/o reweighting
69.2
57.0
Baseline
39.9
69.2
Buse line
69.3
58.1
Ours w/o reweighting
70.1
58.6
Baseline + 2x "motorcycle"
46.9
69.1
Ours
70.7
59.7
Ours
49.7
70.1
Resampling Resampling is not straightforward to implement in semantic segmentation since the training images contain multiple classes. For this experiment, we focus solely on the class with least pixel frequency of Cityscapes ("motoreycle"). without any reweighting scheme. To increase the sampling frequency of the "motorcycle" class, we enlarge the training set such that each image containing the "motorcycle" class is sampled twice at every training epoch. We observe +7%p increase of IoU (39.9% - 46.9%) in the baseline by resampling the "motorcycle" class. However, the baseline with "motorcycle" resampling loses mloU by 0. %pp, meaning that the improvement of the "motorcycle" class comes at the cost of performance on other classes. To the contrary, our model outperforms the baseline with "motorcycle" resampling in terms of both the metrics, by +2.8%p, +1.0% 1.0%pp respectively. The results are shown in Table
3.4 Analysis
Robustness to harmful bias effeets To demonstrate the debiasing effects of DropClass, we conduct a simple experiment to analyze the co-location effects between classes. Since it is difficult to rigorously quantify the co-location and analyze the bias vulnerability of the classes in weal-wrldd segmentation datasets, we define a simple proxy instead: the drop in IoU for each class pair (e.g., IoU "person" after erasing "road") of the baseline model. To achieve this, where the validation set is copied 19 times, and one of the 19 classes in Cityscapes is erased from each copy. By observing how the IoU of class Y is affected by masking out the pixels of class X, we can quantify the harmful effects of co-location. For example, the "person" class is most affected by the absence of "road", "sidewalk" and "bicycle" classes, which suggests that these classes are highly co-located with the "person" class. For each evaluated class, we first identify the Top3 most influential classes, then average IoUs after removing those classes. The same sets of Top3 classes are used to evaluate the model trained with DropClass on the unbiased test set. The results are shown in Table 4 where the baseline model obtains the mloU of 68.9% while our model achieves the mloU of 70.9%. Across the majority classes, we observe that our model suffers from less performance degradation, which verifies that the model trained with DropClass is less susceptible to the negative effects of co-location during inference. Also, note that the "rider" class, by definition, is dependent on the presence of "motorcycle" or "bicycle" class, which may rationalize the reason behind the smaller difference between the baseline and our model.
design an unbiased test set (I*),
7

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Table 4: Quantifying the effects of co-location between classes with respect to bias. Top3 most influential classes are selected from the baseline model. mloU" indicates mloU for the 9 classes with the highest bias vulnerability.
motoreyele
vegetation
rider
wall
t.ighht
bicyele
bus
person
pole
sidewalk
teraan
track
sky
building
road
mloU
mloU>
Baseline ())
62.6
60.5
67.8
54.9
71.0
76.5
87.5
81.7
63.3
85.9
62.6
66.2
73.2
94.3
94.4
91.6
92.2
77.7
98.2
77.0
69.5
Baseline (I*)
33.0 37.8
53.9
40.5
59.2
66.5
80.3
74.3
60.6
79.3
57.7
60.1
68.6
91.8
92.2
89.4
90.0
76.7
97.9
68.9
56.2
A (Baseline)
-2.6 -22.7
11399
-1..4
-11.
10.0
-7.3
-7.
-277 -6.6
4.9
-6.2
-.6
2266
2.2
222
2.1
-1.1
-0.3
-8.0
-13.3
Ours (1)
61.7 61.9
72.6
59.1
72.0
76.9
89.2
82.5
67.8
K5.5
63.1 64.8
72.9
95.2
94.8
92.0
92.7
79.1
98.1
78.0
71.5
Ours (I*)
34.0 43.8
61.0
45.8
62.1
67.1
82.6
76.3
65.2
83.0
57.1
59.1
66.8
92.4
93.5
90.3
90.4
77.9
97,9
70,9
59.8
A (Ours)
27.7 -18.1
-11.6
-13.3
-9.8
-9.8
-6.6
-622
-2.7
-2.5
-6.0
-5.7
-6.0
-2.7
-1.4
-1.6
-2.3
-1.2
-0.2
-7.1 -11.7
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vegetition
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Somrie
013
cst en
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411
ridee
4.3
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traln
10
40
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motorcydce
411
4:30
beyshe
bicydee
5000
perpon
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5000
boffe
Figure 3: Correlation matrices of the classifier weights for the baseline model (left) and the model trained with DropClass (right). The inter-class similarity of our model is clearly smaller than that of the baseline model, which implies that DropClass is effective to climinate the correlation between the representations of similar classes.
Correlation analysis We find that the baseline model is not only prone to learning biases due to the co-location of classes, but also susceptible to learning correlated representations, especially on under-represented classes. Thus, to better illustrate the benefits of DropClass, matrix of both models, where the element at (i,j) corresponds to the cosine similarity between weight vectors of the i-th and j-th clasees in the final classification layer. We notice that the most frequent "road" class has low similarities with all classes, while the least frequent "motorcycle" class has high similarities with multiple classes, such "bicycle" and "rider". To this end, we calculate the row-wise non-diagonal sum, which is a measure of how correlated a class representation is with respect to all other classes and report the numbers in Table 5 We observe that more frequently appearing classes tend to be less correlated with other classes. Note that there is a clear trend between increasing frequency and decreasing correlations among classes. Furthermore, we observe that the model trained with DropClass shows lower inter-class correlations for all class pairs, as summarized in the last row of Table The full correlation matrix is shown Figure
plot the correlation
Grad-CAN CAM visualizations We further analyze our method by examining Grad-CAM visualizations of trained models. We first select three pairs of classes that are likely to confuse a segmentation model: "motorcycle < bicycle", "truck <> train", and 'bus ++ truck". Then we generate the Grad-CAM visualizations with the baseline and the model trained with DropClass (HRNetVI 1 on Cityscapes), and compare the results in Figure 4 where each row corresponds to a different class pair. The second and third columns correspond to the visualizations of the two models when visualizing the Grad-CAMs for the ground-truth (GT) classes while the next two columns present the Grad-CAMs with respect to GT classes. Compared to the visualizations of the baseline model, the visualizations of our model
8

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Table 5: Class correlations. Classes sorted in order of increasing pixel frequency. A indicates
correlations of the baseline model subtracted by correlations of the model trained with DropClass.
motoreyele
vegelation
bicycle"
sky sidewalk
building
rider!
tlligtt
train
bus
truck!
Esign
wall
fence
terrain
person
polc
car
Baseline
1.24 1.20 0.97 0.65 0.91 0.88 0.85 0.60 0.46 0.45 0.53
0.63 0.16 0,45 0.30 0.35 0.33 0.20
Ours
0.74 0.77 0.65 0.47 0.63 0.58
0.52 0.39
0.24 0.23
0.26 0.31
0.28
0.08 0.25 0.19 0.16 0.09 0.12
0.50 0.43 0.32
0.18
0.28
0.30
0.33 0.21
0.22
0.22
0.27 0.17
0.35
0.08
0.20
0.11
0.19
0.24 0.08
Input
Baseline: GT
Ours: GT
Baseline: non-GT
Ours: non-GT
Figure 4: Examples of Grad-CAM visualizations. From top to bottom, the input images contain "motorcycle", "truck", and "bus". The Grad-CAMs of the baseline and the model trained with DropClass with respect to the ground-truth (GT) classes are presented in the second and third columns respectively, while the Grad-CAMs of the two models with respect to the non-GT Hlasses""iiyeel "train", and "truck" are shown in the fourth and fifth columns.
consistently display lower activations for the non-GT classes. Furthermore, our model exhibits strong gradients for the GT classes as well, which indicates that the model maintains better discriminability the process of debiasing.
Ablations To better understand our proposed method and its effec-
Table 6: Ablation experiments on Cityscapes with HRNetV]. 1.
tiveness, we conduct ablation experiments on the Cityscapes dataset with the HRNetV1 model. First, we omit Lup defined in Eq (8), which has the purpose of suppressing outputs for the dropped class. We observe that when we remove this loss term, the effectiveneess of our method decreases slightly in terms of the total mloU and the under-represented class mloU (mloU'), by 0.5%p and 1. 1.1,, respectively. However, still outperforms the baseline by 0.9%p and 0.5%p on the two metrics. Next, we also test the effects of dropping the labels of randomly selected class, without DropClass its accompanying loss functions. other words, model is trained only with the ordinary cross-entropy loss, Eq. 5 while random class labels are not used (e.g., ignored) each iteration. Unsurpris- ingly, observe that this model performs worse than the baseline
Method
mloU
mloUi
Baseline
69.3
58.1
Ours
70.7
59.7
A
1.4
1.6
A (%)
2.0
2.9
w/o Fq. 8
70.2
58.6
A
0.9
0.5
any of
A (%)
1.4
0.9
Ignore Label
69.2
58.0
-0.1
-0.1
(%)
-0.1
-0.1

---

model, by 0.1 %pp in both metrics. Thus, our ablation experiments highlight the importance of the suppression loss and validate the effectiveness of DropClass.
4 Related Works
Dataset bias Dataset bias is a critical issue in modern machine learning because trained models often achieve outstanding accuracy simply by capturing the correlated features instead of identifying the proper representations of target classes. To tackle the limitation, various debiasing techniques have
been proposed under supervised 19 and unsupervised [13 23 24 settings, but most of existing approaches are for simple classification tasks. Technically, they are limited to sample reweighting [24]- 27] and loss adjustment [1]244] The proposed algorithm is unique in the sense that we aim to learn classifier for dense prediction. One previous work [28] argues that feature entanglement in the early layers can enhance the discriminative power of the model. However, they experiment on the classification task, and the conclusion may not be applicable to semantic segmentation, where data samples contain multiple classes per image, We suspect that explicitly entangling features in a segmentation model may have adverse effect on the model's ability to identify class boundaries within a given image.
Semantic segmentation Semantic segmentation works have attempted to improve performance with diverse operations that aim to widen the receptive field, while maintaining details in the features from different resolutions. To this end, DeconvNet
S learns deconvolution layers replace fixed upsampling operations and DeepLabV3 [2] employs atrous convolution to capture objects at multiple scales. Furthermore, PSPNet 20 proposes pyramid pooling module capture different sub-region representations and HRNet [ aggregates four different resolution of feature maps to preserve details, especially for the deeper layers with lower resolutions.
Visual explanations Even with extensive results in broad domains, non-convexity and high- dimensionality of deep neural networks restrict human-level understandings of its internal process. To remedy this issue, 77 32 attempt to make visual explanations of deep models. CAM [30 first proposes find discriminative regions relevant target classes, but has architectural restriction due to the use of the global average pooling layer. Grad-CAM [ visualizes the importance of each class by leveraging the gradient information, and [31 extends Grad-CAM for semantic segmentation. CAM and Grad-CAr have often been employed in weakly supervised object detection [33 34) and
segmentation 37 where they highlight relevant objects or regions without full supervision for target tasks. We compute Grad-CAM for each class, but employ the estimated class-specific feature maps for learning debiased and disentangled representations.
Conclusion and Discussion
We presented model agnostic training scheme that helps the model learn more debiased and disentangled feature representations for the semantic segmentation task. Our method based on the simple and intuitive idea of randomly dropping disentangled class-specific features at each iteration of training. The new combination features removes certain inter-class dependencies, which leads to learning debiased and disentangled feature representations. Our experimental results on two distinct network architectures and datasets validate our method's s effectiveness, and our analysis reflect that method achieves the intended goals.
One limitation of the proposed DropClass training scheme is that it requires more iterations for the model to fully converge. This is because DropClass depends on the model's ability to generate disentangled feature representations and has more complex training process.
Acknowledgments
We truly thank the anonymous reviewers for their valuable comments throughout the review pro- cess. This paper was improved significantly through the communication with them. This work was partly supported by Samsung Advanced Institute of Technology, Korean ICT R&D pro- grams of the MSIT/IITP grant [2017-0-01779, XAI, 2021 0021-0-01343 Artificial Intelligence Graduate School Program (Seoul National University)), and the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korea government (MSIT) (2021M8AA94000782).
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12


Reviews:
[{"review_id": "kJQZl4PBjJ", "reviewer": "Reviewer_4WPr", "summary": "Recent semantic segmentation methods often learn biased representation from the dataset with highly co-located objects (e.g., motorcycle and road) as well as the class imbalance. This paper proposes a training scheme of dropping the class-specific representations in the highly-entangled features, mitigating the bias induced by the dropped class. The proposed DropClass is also model-agnostic and demonstrates the improved segmentation results in Cityscapes and Pascal VOC datasets.", "questions": "", "limitations": "", "rating": 6, "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": "Strength\n1)\tThe paper is well organized and easy to follow. \n2)\tThe main problem setting of the dataset bias in the semantic segmentation task is novel. \n3)\tThe improved IoU scores, particularly for the under-represented classes, are significant.\n\nWeakness\nI think the overall experimental settings and results only show the improved performance on under-represented classes without evaluating the debiased performance between highly correlated classes, which is the main topic addressed in this paper.\n-\tTables 1 and 2 do not contain the information on which classes suffer from the bias. I am concerned that the low class frequency does not necessarily cause the classes to be co-located with other classes. In other words, frequently appearing classes may also suffer from the performance degradation due to the highly correlated object classes.\n-\tSimilarly, are Train-Truck, Truck-Bus, and Bicycle-Motorcycle highly co-located objects in the Cityscapes dataset? Does Vanilla fail because they have the correlation between the classes or because the frequency of such classes is low?\n\nI would like the authors to 1) specify the classes vulnerable to the bias due to the unwanted correlation with other classes and 2) provide the quantitative comparison between classes with bias and without bias. (e.g., class A is often highly co-located with class B, and the segmentation results of A are xxx worse compared to the class C which does not have the unwanted correlation with other classes.) Additionally, the authors need to demonstrate the improved results of these classes by the proposed DropClass.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes. The authors included the limitations and negative societal impact in Section 5.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "48 hours"}, "extra_text": {}}, {"review_id": "YnY6t2TEF9y", "reviewer": "Reviewer_8XQh", "summary": "This paper presents DropClass, a new training scheme for reducing feature dependencies among classes to learn debiased and disentangled feature representations. Toward this, the paper \nconsider Grad-CAM for obtaining the class-specific information, and then randomly discard such information to remove any bias towards a certain class. \n\nA set of experiments on benchmark datasets demonstrate the model’s effectiveness, primarily on under-represented classes. ", "questions": "", "limitations": "", "rating": 6, "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": "Strength:\n* The proposed training scheme is novel, model-agnostic, and straightforward. \n* The experiment covers two crucial datasets in the segmentation literature. Results demonstrate that the inclusion of the proposed training improves over baseline (two well-established frameworks), notably in the under-represented classes. \n \nWeakness:\n* The paper aims to remove the issue of feature entanglement and bias vulnerability. These two topics have been well studied in the current ML/DL literature. In my opinion, the presentation of these topics is a bit hand waving. While reading the paper, one gets the impression that these two are interchangeable. The primary goal seems to make sure the model generalizes well across test sets. If so, is this work trying to use disentanglement to achieve debiased representations and then generalization? I think the paper should be clear enough to answer them.  \n* Lack of comparison against other debiasing techniques. Paper acknowledges and reviews some of them, but no technical comparisons have been made on how such methods would perform in the presented dataset and network architecture. The same applies to disentanglement techniques. \n*  The motivation behind L_sup (Eqn 8) is not clear. The impact of Eqn 8 also looks pretty minimal (Table 3). \n* The methodological contribution is limited. \n* The provided results are not enough to clearly understand the benefit of the proposed training scheme. It is not clear how biased or entangled the baseline framework is before using the proposed training scheme. Only figure 2 is provided to discuss this critical issue. \n \nQuestions:\n* Authors are encouraged to answer/comment about the points raised in the weakness section. \n* Some recent works (Frosst et al. 2019) in disentanglement have pointed out that \"maximizing the entanglement of representations of different classes in the hidden layers is beneficial for discrimination in the final layer, possibly because it encourages representations to identify class-independent similarity structures\". Can authors discuss them in relation to the proposed training approach? \nFrosst, Nicholas, Nicolas Papernot, and Geoffrey Hinton. \"Analyzing and improving representations with the soft nearest neighbor loss.\" International Conference on Machine Learning. PMLR, 2019.\n* Isn't this a training procedure? Why does one need to analyze \"Test time behavior\"? \n \nGrammatical errors:\n* Line 140: \"encouraged learn debiased\" —> \"encouraged to learn debiased\" \n\n********************* Update **********************\nThe score has been updated (5 to 6) after the authors' rebuttal. \n ", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Yes", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4.5"}, "extra_text": {}}, {"review_id": "-SGNSLLshL9", "reviewer": "Reviewer_K1Gz", "summary": "This paper proposes a DropClass training scheme where class-specific feature maps are used to obtain debased and disentangled representations. This is achieved by randomly dropping out class-specific representation from the feature maps, implemented in an annealing manner, reinforced with a regularization term to further remove class-specific information from the remaining information. Experiments are performed on three segmentation networks on two distinct data sets. The presented method seems to achieves an improvement of performance in most classes, with the most noticeable margin of improvement in minority classes.", "questions": "", "limitations": "", "rating": 8, "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": "The presented concept is interesting and original. The methodology is well motivated and clearly described. Experiment results are promising. Some technical questions can be clarified. \n\nFirst, to make sure that the gradient estimated is reasonable, the authors mentioned that the loss function slowly incorporates DropClass \"by linearly increasing the vlaue of lambda from 0 to 1 across the duration of training\". How is this linear rate determined exactly, assuming that the training is terminated based on some validation loss and thus the number of epochs needed is not known in advance. Or is the linear ratio pre-determined and irrelevant to the number of training epochs? Furthermore, since it is important to wait until the gradient estimation is reasonably accurate, would some exponential or other types of increase more effective than a linear increase rate?\n\nTo avoid heavy computation associated with gradient calculation for each class, the authors focused on convolution layers and implemented the final classification layer using 1x1 convolution as well. Does this mean the presented method will be restricted to convolutional networks? \n\nIn experimental results in Pscal, it seems that nearly half to the classes were negatively impacted. What is th implication? Does it suggest that DropClass should be targeting certain classes only rather than all classes, or targeting different classes with different weights? If so, how?\n\nOverall this is a very well written paper that presents an original idea in a simple fashion. It has high discussion potential. ", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "The authors adequately addressed the limitations and potential negative societal impact of their work.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This work describes a new training scheme for semantic segmentation models (“DropClass”) that is intended to encourage the learning of feature representations that avoid entangling features from objects (e.g. car & road) that tend to co-occur/co-locate. 

Reviewers were generally positive about the work, and found it to be interesting and novel. Reviewers and authors had something of a philosophical debate about the nature of dataset bias and entanglement, and it will be helpful for the authors to clarify these terms in the resulting paper, and to ensure that limitations of the method (e.g. tendency for low frequency classes to become entangled with semantically similar class) are appropriately highlighted for readers. All in all, this is strong work that should be of interest to the NeurIPS community.

Decision: Accept (Poster)