深度可解释CNN：对象分类中的特征编码方法
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更新于2024-07-16
收藏 8.14MB PDF 举报 本文主要探讨了"可解释CNN的对象分类"这一前沿研究领域，由上海交通大学的研究团队提出。该研究聚焦于如何在深度卷积神经网络（Deep Convolutional Neural Networks, CNN）中实现可解释性，即让机器学习模型的决策过程更加透明，以便用户理解模型是如何识别和分类对象的。传统的CNN在图像分类任务上表现出色，但缺乏对内部工作原理的直观解释，这限制了其在某些领域的应用，如医疗诊断、安全监控等，需要对模型决策有明确依据。
研究人员提出了一种创新的方法，旨在学习并生成可解释的卷积滤波器。这些滤波器专门针对CNN中的特定对象部分编码特征，例如，一个滤波器可能专门负责检测猫的耳朵或狗的尾巴。这种方法的独特之处在于它不需要额外的人工标注来指导学习，而是利用与传统CNN相同的训练数据集进行训练。这意味着，通过自然的监督学习过程，模型能够自我学习和理解对象的不同组成部分，并在高层次的卷积层中分配这些滤波器，以对应不同的物体类别。
这种方法的价值在于，它不仅提升了模型的分类性能，还提供了关于CNN如何从输入图像中提取特征以及如何基于这些特征做出预测的深入了解。通过可解释的滤波器，人们可以清晰地看到模型关注的图像区域，有助于增强信任度和模型的可接受性。为了验证其普适性，研究者在不同结构的基准CNN上进行了实验，结果显示，生成的可解释滤波器具有更高的语义意义，从而证明了这种方法的有效性和广泛适用性。
这项研究为提高深度学习模型的透明度和理解性提供了一种实用且有效的途径，有助于推动计算机视觉和人工智能领域的进步，特别是在那些需要可靠决策支持的应用场景中。
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 4
object part of a certain category, while remain inac-
tivated on images of other categories
1
. Let I denote
a set of training images, where I
c
⊂ I represents the
subset that belongs to category c, (c = 1, 2, . . . , C).
Theoretically, we can use different types of losses to
learn CNNs for multi-class classification and binary
classification of a single class (i.e. c = 1 for images of
a category and c = 2 for random images).
In the following paragraphs, we focus on the learn-
ing of a single filter f in a conv-layer. Fig. 2 shows
the structure of our interpretable conv-layer. We add a
loss to the feature map x of the filter f after the ReLU
operation. The filter loss Loss
f
pushes the filter f to
represent a specific object part of the category c and
keep silent on images of other categories. Please see
Section 3.2 for the determination of the category c for
the filter f . Let X = {x|x = f (I) ∈ R
n×n
, I ∈ I} denote
a set of feature maps of f after an ReLU operation
w.r.t. different images. Given an input image I ∈ I
c
,
the feature map in an intermediate layer x = f(I) is
an n × n matrix, x
ij
≥ 0. If the target part appears,
we expect the feature map x = f (I) to exclusively
activate at the target part’s location; otherwise, the
feature map should keep inactivated.
Therefore, a high interpretability of the filter f re-
quires a high mutual information between the feature
map x = f(I) and the part location, i.e. the part
location can roughly determine activations on the
feature map x.
Accordingly, we formulate the filter loss as the
minus mutual information, as follows.
Loss
f
=−MI(X; Ω) = −
X
µ∈Ω
p(µ)
X
x
p(x|µ) log
p(x|µ)
p(x)
(1)
where MI(·) denotes the mutual information; Ω =
{µ
1
, µ
2
, . . . , µ
n
2
} ∪ {µ
−
}. We use µ
1
, µ
2
, . . . , µ
n
2
to
denote the n
2
neural units on the feature map x, each
µ = [i, j] ∈ Ω, 1 ≤ i, j ≤ n, corresponding to a location
candidate for the target part. µ
−
denotes a dummy
location for the case when the target part does not
appear on the image.
Given an input image, the above loss forces each
filter to match and only match one of the templates,
i.e. making the feature map of the filter contain a
single significant activation peak at most. This ensures
each filter to represent a specific object part.
• p(µ) measures the probability of the target part
appearing at the location µ. If annotations of part
locations are given, then the computation of p(µ) is
simple. People can manually assign a semantic part
with the filter f, and then p(µ) can be determined
using part annotations.
However, in our study, the target part of filter f is
not pre-defined before the learning process. Instead,
1. To avoid ambiguity, we evaluate or visualize the semantic
meaning of each filter by using the feature map after the ReLU
and mask operations.
the part corresponding to f needs to be determined
during the learning process. More crucially, we do not
have any ground-truth annotations of the target part,
which boosts the difficulty of calculating p(µ).
• The conditional likelihood p(x|µ) measures the
fitness between a feature map x and the part location
µ ∈ Ω. In order to simplify the computation of p(x|µ),
we design n
2
templates for f, {T
µ
1
, T
µ
2
, . . . , T
µ
n
2
}.
As shown in Fig. 3, each template T
µ
i
is an n × n
matrix. T
µ
i
describes the ideal distribution of acti-
vations for the feature map x when the target part
mainly triggers the i-th unit in x. In addition, we also
design a negative template T
−
corresponding to the
dummy location µ
−
. The feature map can match to
T
−
, when the target part does not appear on the input
image. In this study, the prior probability is given as
p(µ
i
) =
α
n
2
, p(µ
−
) = 1 − α, where α is a constant prior
likelihood.
Note that in Equation (1), we do not manually
assign filters with different categories. Instead, we use
the negative template µ
−
to help the assignment of
filters. I.e. the negative template ensures that each
filter represents a specific object part (if the input
image does not belong to the target part, then the
input image is supposed to match µ
−
), which also
ensures a clear assignment of filters to categories.
Here, we assume two categories do not share object
parts, e.g. eyes of dogs and those of cats do not have
similar contextual appearance.
We define p(x|µ) below, which follows a standard
form widely used in [25], [38].
p(x|µ) ≈ p(x|T
µ
) =
1
Z
µ
exp
tr(x · T
µ
)
(2)
where Z
µ
=
P
x∈X
exp[tr(x · T
µ
)]. tr(·) indicates the
trace of a matrix, and tr(x · T
µ
) =
P
ij
x
ij
t
ji
, x, T
µ
∈
R
n×n
. p(x) =
P
µ
p(µ)p(x|µ).
Part templates: As shown in Fig. 3, a negative
template is given as T
−
= (t
−
ij
), t
−
ij
= −τ < 0,
where τ is a positive constant. A positive template
corresponding to µ is given as T
µ
= (t
+
ij
), t
+
ij
=
τ · max(1 − β
k[i,j]−µk
1
n
, −1), where k · k
1
denotes the
L-1 norm distance. Note that the lowest value in a
positive template is -1 instead of 0. It is because that
the negative value in the template penalizes neural ac-
tivations outside the domain of the highest activation
peak, which ensures each filter mainly has at most a
single significant activation peak.
3.1 Part localization & the mask layer
Given an input image I, the filter f computes a feature
map x after the ReLU operation. Without ground-
truth annotations of the target part for f , in this
study, we determine the part location on x during the
learning process. We consider the neural unit with the
strongest activation ˆµ = argmax
µ=[i,j]
x
ij
, 1 ≤ i, j ≤ n
as the target part location.
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