FILTRA: R ETHINKING S TEERABLE CNN BY F ILTER T RANSFORM
Anonymous authors
Paper under double-blind review
A BSTRACT
Steerable CNN imposes the prior knowledge of transformation invariance or equivariance in the network architecture to enhance the the network robustness on geometry transformation of data and reduce overﬁtting.
Filter transform has been an intuitive and widely used technique to construct steerable CNN in the past decades.
Recently, group representation theory is used to analyze steerable CNN and reveals the function space structure of a steerable kernel function.
However, it is not yet clear on how this theory is related to the ﬁlter transform technique.
In this paper, we show that kernel constructed by ﬁlter transform can also be interpreted in the group representation theory.
Meanwhile, we show that ﬁlter transformed kernels can be used to convolve input/output features in different group representation.
This interpretation help complete the puzzle of steerable CNN theory and provides a novel and simple approach to implement steerable convolution operators.
Experiments are executed on multiple datasets to verify the feasibilty of the proposed approach.
I NTRODUCTION
Beyond the well-known property of equivariance under translation, there has been substantial recent interest in CNN architectures that are equivariant with respect to other transformation groups, e.g. reﬂection and rotation.
Applications of such architectures range over scenarios where object orientation might variate, including OCR, aerial image processing, 3D point cloud processing, medical image processing, texture analysis and etc.
Previous works on constructing equivariant CNN can be coarsely categorized as two aspects.
The ﬁrst aspect designs special steerable ﬁlters so that the convolutional output is hard-baked to transform accordingly when the input reﬂects or rotates.
A plenty of works develop this idea by ﬁlter rotation, including hand-crafted ﬁlters (Oyallon & Mallat, 2015) and learned ﬁlters (Laptev et al., 2016;
Zhou et al., 2017; Cheng et al., 2018; Marcos et al., 2017).
TI-Pooling (Laptev et al., 2016) produce invariant output as input rotates.
ORN (Zhou et al., 2017) and RotDCF (Cheng et al., 2018) produces output which circularly shifted as input rotates.
Since each dimension of such permutable output corresponds to a uniformly discrete rotation angle, RotEqNet (Marcos et al., 2017) propose to extract rotation angle from the permutable features.
Another approach to construct steerable ﬁlters is to linearly combine a set of steerable bases.
These basis can be solved in discrete function space (Cohen & Welling, 2014; 2016) or continuous function space (Worrall et al., 2017; Weiler & Cesa, 2019).
Weiler & Cesa (2019) comprehensively summarize works on steerable bases using polar Fourier basis.
The second aspect exploits speciﬁc transforms to act on input.
Spatial Transformer Network (STN) is a well-known representative, which predicts an afﬁne matrix to transform its input to the canonical form.
Tai et al.
(2019) inherits this idea to design equivariant network.
Another choice of transform is to the polar coordinate system (Henriques & Vedaldi, 2017; Esteves et al., 2018).
Since 2D rotation in Cadestrian coordinate system corresponds to 2D translation in polar coordinate system, rotation equivariance can be achieved by conventional translation equivariant CNN.
The approach proposed in this paper falls into the ﬁrst category.
Weiler & Cesa (2019) proves that all steerable convolutional operator could be denoted as the combination of a speciﬁc set of polar
Fourier bases.
However, it is not clear yet how this interpretation is related with the widely used ﬁlter transform scheme.
In this paper, we aim to establish the missing connection between the group representation based analysis for steerable ﬁlters and ﬁlter transform scheme.
To this end, we propose a new approach (FILTRA) to use ﬁlter transform to establish steerability between features in different group representation in cyclic group C N and dihedral group D N .
We verify the feasibility of FILTRA for the classiﬁcation and regression tasks on different datasets.
P RELIMINARIES
We make use of several NumPy or SciPy functions in equations including roll 1 , flipud 2 and circulant 3 .
We omit the variable in bracket sometimes by writing κ ∗∗ = κ ∗∗ ( g ) and K ∗∗ = K ∗∗ ( φ ) . 2. S TEERABLE CNN [Equation]
We recapitulate the basic concepts of steerable
CNN which will be frequently used in this paper.
For detailed introduction, readers can refer to Weiler & Cesa (2019) for a comprehensive information.
We mainly consider the 2D image case and denote x ∈ R 2 as a pixel coordinate. We use vector ﬁeld f ( x ) ∈ R C to denote a general multi-channel image, where C is the number of channels. Typical examples of f ( x ) include RGB image f ( x ) ∈ R 3 and gradient image f ( x ) ∈ R 2 .
Consider a group G of transformations and an element g ∈ G .
Examples of G include rotation, translation and ﬂip.
A vector ﬁeld f ( x ) follows the below rules when undergoing the act π ( g ) of a group element g :
[Equation]
(1) where ρ ( g ) is a group representation related to vector ﬁeld f .
Fig.
1 shows an example of different types of ρ for RGB images and gradient images under a rotation transform element g .
The group representation of RGB is ρ ( g ) ≡while for gradient image ρ ( g ) is a 2D rotation matrix which also rotates vector f
( x ) by g .
In the scenario of convolutional neural network, a convolution operator f
(cid:55)→ κ · f is considered as steerable if it satisﬁes
[Equation]
i.e. the output vector ﬁeld transforms equivariantly under g when the input is transformed by g .
2. R EFLECTION G ROUP , C YCLIC G ROUP AND D IHEDRAL G ROUP
We consider steerable ﬁlters on reﬂection group ( {± 1 } , ∗ ) , cyclic group C N and dihedral group
D N = ( {± 1 } , ∗ ) (cid:110) C N .
To unify the notations in derivation, we interpret C N = ( { 1 } , ∗ ) (cid:110) C N and ( {± 1 } , ∗ ) =
( {± 1 } , ∗ ) (cid:110) C
= D 1 so that a element in these three groups can always be denoted as a pair g
= ( i 0 , i 1 ) , whose range is Z 2 ×
Z 1 for reﬂection group, Z 1 × Z N for cyclic group and Z 2 × Z N for dihedral group.
Each element in C N corresponds to rotation angle θ i 1 = 2 i 1 πN .
2. G ROUP R EPRESENTATION
A linear representation ρ of a group G on a vector space R n is a group homomorphism from G to the general linear group GL( n ) , denoted as
[Equation]
We consider three types of linear representation in this paper, i.e. trivial representation, regular representation and irreducible representation (irrep).
Readers can refer to Serre (1977) for further background for these concepts.
The trivial representation of a group element is always ρ tri ( g ) ≡ 1 .
The regular representation of a ﬁnite group G acts on a vector space R | G | by permuting its axis.
Therefore, for a rotation element g = (0 , i 1 ) ∈ C N or D N , we get
[Equation]
where P ( i 1 ) =
roll ( I N , i 1 , 0) .
[Equation]
For a reﬂected element g = (1 , i 1 ) ∈ D N , we get
[Equation]
, where B ( i 1 ) = flipud ( P ( − i 1 − 1)) .
[Equation]
[Equation]
By selecting suitable change of basis of the vector space, a representation can be converted to a equivalent representation, which is the direct sum of several independent representations on the orthogonal subspaces.
A representation is called irreducible representation if no non-trivial decomposition exists.
This conversion is denoted as
[Equation]
where I is an index set specifying the irreducible repsentations ψ i and Q is the change of basis.
2. D ECOMPOSING R EGULAR R EPRESENTATION
We decompose the regular representation into a set of irreps.
Deﬁne the following base irrep
[Equation]
where j, k are referred as the reﬂection and rotation frequency of the irrep.
Concretely, if the action g reﬂects/rotates an object once, ψ j,k ( g ) will reﬂects/rotates in vector space j/k times.
We also deﬁne the following discrete cosine transform basis
[Equation]
where β k =
[Equation]
The following decomposition for ρ C N reg (0 , i 1 ) holds
[Equation]
The decomposition for ρ D N reg ( i 0 , i 1 ) holds in a bit more complicated form, i.e.
[Equation]
where W =
[Equation]
[Equation]
and each column of W is refered by β j,k = (cid:2) β (cid:62) k
[Equation]
β
[Equation]
See Fig.
2 for a visualization of this decomposition.
We also mention a property of β k that is easy to verify and will be useful in our derivation.
[Equation]
11b
[Equation]
where ψ 0 ,k ( i 0 , i 1 ) rotates column vectors of β (cid:62)
k
as if they are circularly shifted.
[Equation] 2. H ARMONIC F ILTERS
Weiler et al.
(2018) proposes the condition of a ﬁlter kernel κ to be equivariant under the action g ∈ G .
Lemma 1.
The map f
(cid:55)→ κ · f is equivariant under G if and only if for all g ∈ G ,
[Equation]
Weiler & Cesa (2019) proves that such ﬁlters can be denoted by a series of harmonic bases b ( φ ) , i.e.
[Equation]
where ω b ( r ) is the per radial weights and K is a set of harmonic bases as dervied in the appendix of Weiler & Cesa (2019).
For example, consider ρ in = ψ i,m and ρ out = ψ j,n in D N ,
[Equation]
M AIN R ESULTS
(12) and (13) provide a general approach to verify and construct steerable CNN with different representations.
In this section, we relate these theories with ﬁlter transform and show how to use ﬁlter transform to construct steerable ﬁlters with input/output of different representations.
For readers who are not interested in group theory and mathematical derivation of the theory connection, we highlight the key equations to construct steerable ﬁlters in boxes. It should not be difﬁcult to implement steerable ﬁlters directly from these equations using any modern deep learning framework. Fig.
3 shows illustration for these equations.
In our derivation, we mainly consider the angular coordinate of polar coordinate functions κ ( r, φ ) and write them κ ( φ ) .
We will also frequently make use of the following property:
[Equation]
3. F ROM T RIVIAL R EPRESENTATION TO R EGULAR R EPRESENTATION Rotation Group C N
Consider the the rotating ﬁlter K and its reﬂected version K which are commonly used in previous works, e.g. TI-Pooling, ORN, RotEqNet and RotDCF:
[Equation]
The output of convolution with the above kernels naturally permutes as the input rotates in C N .
This intuitively corresponds to property of a steerable ﬁlter transforming from trivial representation to regular representation.
In this paper, we use K and K as the basic ﬁlters to construct different types of steerable ﬁlters in C N and D N .
We verify the observation of the above steerability by substituting
K into the lhs of Lemma 1 with g = (0 , 1) and write:
[Equation]
N reg (0 , 1) K ρ tri (0 , 1) − 1 .
(17b)
[Figure]
The above equation can be similarly veriﬁed for other g =
(0 , i 1 ) and also on K .
Thus WLOG we select the steerable ﬁlter which transforms trivial representation to regular representation on C N as [Equation]
[Equation]
Dihedral Group D N
The steerable ﬁlter that transforms trivial representation to regular representation on D N can be constructed as
[Equation]
(19) which corresponds to enumerating each D N element and act on the kernel κ .
For g = (0 , i 1 ) , K D N 0 → reg can be veriﬁed to follow (12) in the same way as (17a), i.e. K D N [Equation] → reg [Equation]
+
[Equation]
=
[Equation] N
[Equation]
For reﬂected action, when g = (1 , 1) , we write:
[Equation]
Similarly, we can show for g
=
(1 , i 1 ) ,
[Equation]
Thus we verify (12) for the reﬂected actions g = (1 , i 1 ) by summarizing the above as K D N 0 → reg ( − φ + θ i 1 ) =
{+ρ D N reg ( g ) K D N 
 → reg ρ tri ( g ) − 1 .+}
3. F ROM I RREP TO R EGULAR R EPRESENTATION Rotation Group C N
Consider a C N irrep ψ 0 ,k ( g ) with frequency (0 , k ) .
We show that the following kernel
[Equation]
transforms from ψ 0 ,k ( g ) to regular representation for the action g
=
(0 , i 1 ) .
The derivation of correctness can be found in the appendix.
Dihedral Group D N
Consider a D N irrep ψ j,k
( i 0 , i 1 ) with frequency ( j, k ) .
We show that the following kernel:
[Equation]
23) transforms from ψ j,k
( i 0 , i 1 ) to regular representation for the action g
= ( i 0 , i 1 )
∈ D N .
[-→ reg follows Lemma 1 for j = 0 , i 
-]
[-The veriﬁcation is similar for j = 1 , i 
-]
3. F ROM R EGULAR R EPRESENTATION TO R EGULAR R EPRESENTATION
Regular representation possesses a nice property that it can be averaged, pooled or activated channelwise without violating steerability (Weiler & Cesa, 2019).
Thus it is convenient to used regular representation for the intermediate features of a steerable CNN.
We show in this subsection that the following kernels can be use to construct a steerable kernel whose input and output features are both in regular representation.
Rotation
[Equation]
Dihedral Group D N
[Equation]
The above two kernels can be veriﬁed to transform regular representation to regular representation in similar way and we show the derivation for the C N case (25) as an example in the appendix.
3. R EVERSED T RANSFORM OF R EPRESENTATIONS
It is obvious to ﬁnd that for (12), if ρ in , ρ out are orthogonal matrices, i.e. ρ − 1 in = ρ (cid:62) in , ρ − 1 out
= ρ (cid:62) out , the transpose of (12) naturally proves the equivariance of κ (cid:62) under a reversed representation transform direction, i.e. from ρ out to ρ in .
Thus we can easily obtain equivariance kernel from regular representation to trivial/irreduciable representation by simply transposing (18), (19), (22) and (23).
3. C ONVENTIONAL R OTATING F ILTERS
We comprehensively study the approach to use ﬁlter rotation to form steerable convolutional kernels with regular representation features as input or output.
Conventional ﬁlter rotation based networks exploit some basic forms introduced in this section.
TI-Pooling (Laptev et al., 2016) exploits kernel
K C N to transform trivial to regular representation, executes orientation pooling to convert regular to trivial representation and loses orientation information.
RotDCF and ORN exploits a kernel of form
[Equation]
It is easy to verify that K C N ORN also follows Lemma 1 to be a steerable ﬁlter.
However, compared to
K C N reg → reg , K C N ORN consumes same ﬁlter storage but has less weight capacity ( N v.s. N (cid:98)
N 2 (cid:99) ).
RotEqNet constructs 2D vector ﬁeld which could rotate as its input rotates but regards the 2D vector ﬁeld as independent trivial representation in convolution.
As shown in this paper, it preserves better steerability to regards the vector ﬁeld as irrep representation with frequency 1 .
3. N UMERICAL A CCURACY FOR D ISCRETE K ERNELS
Note that when implementing discrete convolution, the equality of (17a) does not perfectly hold.
For example, consider κ n ( φ ) =
κ ( φ − θ n ) , κ n ( θ n ) = κ (0) holds for a continuous κ .
However, for discrete κ , κ n ( φ ) is a rotated interpolation of κ ( φ ) and this equity does not precisely hold in general.
There exist some exceptions where the equity can be achieved for discrete κ .
One example is when κ n ( φ ) is a 90 ◦ rotation of κ and it can be precisely constructed from κ .
Another example is when κ n is a 45 ◦ rotation interpolated by nearest pixel from a κ of size 3 × 3 . [Table] [Table] [Table]
[Table]
[Table]
, 2019) to summarize feature in regular representation. 3. S TEERABLE CNN WITH M ULTIPLE L AYERS A conventional CNN is usually composed convolution, pooling, nonlinearity and fully-connected layers. To achieve equivariance for the overall network, it is desired that all the component layers are steerable.
As analyzed in the appendix of Weiler & Cesa (2019), channel-wise nonlinearity and channel-wise pooling preserves the steerability on feature maps with regular representation.
fullyconnected layers is a special case of convolution with 1 × 1 kernels and thus can be easily realized by steerable convolution.
The proposed equivariant convolution, refered as FILTRA, can be interpreted as an alternative formulation for the harmonic based (Weiler & Cesa, 2019) implementation of steerable convolution.
In this section we show the pros and cons of each implementation by experiments.
We make use of the framework E2CNN (Weiler & Cesa, 2019) for our experiments as it provides the general interface and operations for steerable CNN network.
Experiments are executed on the MNIST, KMNIST (Clanuwat et al., 2018), FashionMNIST (Xiao et al., 2017), EMNIST (Cohen et al., 2017) and
CIFAR10 datasets.
We compare FILTRA against two convolution operations, i.e. the representative harmonic based convolution R2Conv (Weiler & Cesa, 2019) from E2CNN and the conventional vanilla convolution.
All MNIST-like datasets are experimented on a same feature extraction backbone as described in Table 1a, with convolution operator realized by the three experimented approaches.
CIFAR10 is experimented with WideResNet (Zagoruyko & Komodakis, 2016) in the setting similar to Weiler & Cesa (2019).
We found that on CIFAR10, C 4 steerable network performs better than C 8 for both approaches.
For all experiments, we randomly rotate or reﬂect according to the experiment settings.
The settings and evaluation results are listed in Table 2.
Different from Weiler et al.
(2018), we force the three convolution kernels to output same number of channels.
For example, compared to vanilla convolution, the number of free weights for a C 8 FILTRA is reduced to 1 / 8 and for a D 8 is reduced to 1 / 16 .
The ﬁlters for all the approaches will thus have exactly same shape at the deploy stage.
Experiments are executed on GTX 2070.
The training procedure of FILTRA and R2Conv can both be implemented as a vanilla convolution plus a ﬁlter generation step.
For C 8 case the runtime of both generator is similar and for D 8 case FILTRA is slightly faster.
We show runtime of D 8 case in
Table 1a at training stage.
R2Conv additionally requires a initialization of about 2 min.
Both of the approaches consume the same inference time as of vanilla convolution.
[Table]
4. C LASSIFICATION T ASK
The most typical experiment used in previous works on conventional steerable CNN is the classiﬁcation task.
We follow this convention and compare the classiﬁcation performance of the experimented three approaches in Table 2.
FILTRA show comparable performance to R2Conv and slightly improves accuracy for OCR-like (*MNIST) tasks where high frequency texture is limited.
On CIFAR10, the performance of FILTRA is minorly disadvantageous.
The explanation comes in the interpolation artifacts mentioned in Subsect.
As the interpolation of high frequency components deviates more, this harms the performance on CIFAR10 with high frequency texture
. 4. R EGRESSION T ASK
Besides the typical classiﬁcation task, we ﬁnd that the property of steerability is naturally advantageous for many regression tasks whose input might rotate or reﬂect.
In this paper, we evaluate the regression performance with an example task to predict the character direction.
Similar tasks are commonly used in OCR techniques.
When the character rotates, the predicted direction should rotate with the same rotating frequency.
This means the predicted 2D direction vector is following a irrep ψ 0 , 1 for C N .
We reuse the backbone in Table 1a to extract features and use a regression head in Table 1c to predict a unit 2D vector denoting the direction.
The network is trained with MSE loss.
Note that the images should be masked by a disk to avoid the network to overﬁt the direction from rotated black boundary.
Different approaches are evaluated by the mean included angle between the predicted and groundtruth directions as shown in Table 2.
FILTRA with C 8 steerability performs best when trained on data augmented over SO(2) .
We owe this to the fact that FILTRA weight is naturally organized by the discrete grid layout.
Each element of discrete weight matrix contribute to one more DoF of the ﬁlters.
In contrast, R2Conv uses ﬁlters parameterized with a polar coordinate.
The DoF of the ﬁlters is slightly reduced due to the discretization.
C ONCLUSIONS
In this paper, we establish the connection between the recent steerable CNN structure based on group representation theory and the conventional transformed ﬁlters.
To this end, we propose an approach to construct steerable convolution ﬁlters, which transform between features in trival, irreduciable and regular representations.
We verify the feasibility of FILTRA for the classiﬁcation and regression tasks on several datasets.