Graph Convolution with Low-rank Learnable Local Filters
Anonymous authors
Paper under double-blind review
Abstract
Geometric variations like rotation, scaling, and viewpoint changes pose a signiﬁcant challenge to visual understanding.
One common solution is to directly model certain intrinsic structures, e.g., using landmarks.
However, it then becomes non-trivial to build eﬀective deep models, especially when the underlying non-Euclidean grid is irregular and coarse.
Recent deep models using graph convolutions provide an appropriate framework to handle such non-Euclidean data, but many of them, particularly those based on global graph Laplacians, lack expressiveness to capture local features required for representation of signals lying on the non-Euclidean grid.
The current paper introduces a new type of graph convolution with learnable low-rank local ﬁlters, which is provably more expressive than previous spectral graph convolution methods.
The model also provides a uniﬁed framework for both spectral and spatial graph convolutions.
To improve model robustness, regularization by local graph Laplacians is introduced.
The representation stability against input graph data perturbation is theoretically proved, making use of the graph ﬁlter locality and the local graph regularization.
Experiments on spherical mesh data, real-world facial expression recognition/skeleton-based action recognition data, and data with simulated graph noise show the empirical advantage of the proposed model.
Introduction
Deep methods have achieved great success in visual cognition, yet they still lack capability to tackle severe geometric transformations such as rotation, scaling and viewpoint changes.
This problem is often handled by conducting data augmentations with these geometric variations included, e g. by randomly rotating images, so as to make the trained model robust to these variations.
However, this would remarkably increase the cost of training time and model parameters.
Another way is to make use of certain underlying structures of objects, e.g. facial landmarks (Chen et al., 2013) and human skeleton landmarks (Vemulapalli et al., 2014a), c.f. Fig.
1 (right).
Nevertheless, these methods then adopt hand-crafted features based on landmarks, which greatly constrains their ability to obtain rich features for downstream tasks.
One of the main obstacles for feature extraction is the non-Euclidean property of underlying structures, and particularly, it prohibits the direct usage of prevalent convolutional neural network (CNN) architectures (He et al., 2016; Huang et al., 2017).
Whereas there are recent CNN models designed for non-Euclidean grids, e.g., for spherical mesh (Jiang et al., 2019; Cohen et al., 2018; Coors et al., 2018) and manifold mesh in computer graphics (Bronstein et al., 2017; Fey et al., 2018), they mainly rely on partial diﬀerential operators which only can be calculated precisely on ﬁne and regular mesh, and may not be applicable to the landmarks which are irregular and course.
Recent works have also applied
Graph Neural Network (GNN) approaches to coarse non-Euclidean data, yet methods using
GCN (Kipf & Welling, 2016) may fall short of model capacity, and other methods adopting GAT (Veliˇckovi´c et al., 2017) are mostly heuristic and lacking theoretical analysis.
A detailed review is provided in Sec. 1.1.
In this paper, we propose a graph convolution model, called L3Net , originating from lowrank graph ﬁlter decomposition, c.f. Fig.
1 (left).
The model provides a uniﬁed framework for graph convolutions, including ChebNet (Deﬀerrard et al., 2016), GAT, EdgeNet (Isuﬁ et al., 2020) and CNN/geometrical CNN with low-rank ﬁlter as special cases.
In addition, we theoretically prove that L3Net is strictly more expressive to represent graph signals than spectral graph convolutions based on global adjacency/graph Laplacian matrices, which is then empirically validated, c.f.
Sec.
We also prove a Lipschitz-type representation stability of the new graph convolution layer using perturbation analysis.
Because our model allows neighborhood specialized local graph ﬁlters, regularization may be needed to prevent over-ﬁtting, so as to handle changing underlying graph topology and other graph noise, e.g., inaccurately detected landmarks or missing landmark points due to occlusions.
Therefore, we also introduce a regularization scheme based on local graph
Laplacians, motivated by the eigen property of the latter.
This further improves the representation stability aforementioned.
The improved performance of L3Net compared to other
GNN benchmarks is demonstrated in a series of experiments, and with the the proposed graph regularization, our model shows robustness to a variety of graph data noise.
In summary, the contributions of the work are the following:
• We propose a new graph convolution model by a low-rank decomposition of graph ﬁlters over trainable local basis, which uniﬁes several previous models of both spectral and spatial graph convolutions.
• Regularization by local graph Laplacians is introduced to improve the robustness against graph noise.
• We provide theoretical proof of the enlarged expressiveness for representing graph signals and the Lipschitz-type input-perturbation stability of the new graph convolution model.
• We demonstrate with applications to object recognition of spherical data and facial expression/skeleton-based action recognitions using landmarks.
Model robustness against graph data noise is validated on both real-world and simulated datasets.
1. Related Works
Modeling on face/body landmark data.
Many applications in computer vision, such as facial expression recognition (FER) and skeleton-based action recognition, need to extract high-level features from landmarked data which are sampled at irregular grid points on human face or at body joints.
While CNN methods (Guo et al., 2016; Ding et al., 2017; Meng et al., 2017) prevail in FER task, landmark methods have the potential advantage in lighter model size as well as more robustness to previously mentioned geometric transformations like pose variation.
Earlier methods based on facial landmarks used hand-crafted features (Jeong & Ko, 2018; Morales-Vargas et al., 2019) rather than deep networks.
Skeleton-based methods in action recognition have been developed intensively recently (Ren et al., 2020), including non-deep methods (Vemulapalli et al., 2014b; Wang et al., 2012) and deep methods (Ke et al., 2017; Kim & Reiter, 2017; Liu et al., 2016; Yan et al., 2018).
Facial and skeleton landmarks only give a coarse and irregular grid, and then mesh-based geometrical CNN’s are hardly applicable, while previous GNN models on such tasks may lack suﬃcient expressive power.
Graph convolutional network.
A systematic review can be found in several places, e.g.
Wu et al.
Spectral graph convolution was proposed using full eigen decomposition of the graph Laplacian in Bruna et al.
(2013), Chebyshev polynomial in ChebNet (Deﬀerrard et al., 2016), by Cayley polynomials in Levie et al.
GCN (Kipf & Welling, 2016), the mostly-used GNN, is a variant of ChebNet using degree-1 polynomial.
Liao et al.
(2019) accelerated the spectral computation by Lanczos algorithm.
Spatial graph convo- lution has been performed by summing up neighbor nodes’ transformed features in NN4G
(Scarselli et al., 2008), by graph diﬀusion process in DCNN (Atwood & Towsley, 2016), where the graph propagation across nodes is by the adjacency matrix.
Graph convolution with trainable ﬁlter has also been proposed in several settings: MPNN (Gilmer et al., 2017) enhanced model expressiveness by message passing and sub-network; GraphSage (Hamilton et al., 2017) used trainable diﬀerential local aggregator functions in the form of LSTM or mean/max-pooling; GAT (Veliˇckovi´c et al., 2017) and variants (Li et al., 2018;
Zhang et al., 2018; Liu et al., 2019) introduced attention mechanism to achieve adaptive graph aﬃnity, which remains non-negative valued; EdgeNet (Isuﬁ et al., 2020) developed adaptive ﬁlters by taking products of trainable local ﬁlters.
Our model learns local ﬁlters which can take negative values and contains GAT and EdgeNet as special cases.
Theoretically, expressive power of GNN has been studied in Morris et al.
; Xu et al.
; Maron et al.
(2019a;b); Keriven & Peyr´e (2019), mainly focusing on distinguishing graph topologies, while our primary concern is to distinguish signals lying on a graph.
CNN and geometrical CNN.
Standard CNN applies local ﬁlters translated and shared across locations on an Euclidean domain.
To extend CNN to non-Euclidean domains, convolution on a regular spherical mesh using geometrical information has been studied in S2CNN (Cohen et al., 2018), SphereNet (Coors et al., 2018), SphericalCNN (Esteves et al., 2018), and UGSCNN (Jiang et al., 2019), and applied to 3D object recognition, for which other deep methods include 3D convolutional (Qi et al., 2016) and non-convolutional architectures (Qi et al., 2017a;b).
CNN’s on manifolds construct weight-sharing across local atlas making use of a mesh, e.g., by patch operator in Masci et al.
(2015), anisotropic convolution in ACNN (Boscaini et al., 2016), mixture model parametrization in MoNet (Monti et al., 2017), spline functions in SplineCNN (Fey et al., 2018), and manifold parallel transport in
Schonsheck et al.
These geometric CNN models use information of non-Euclidean meshes which usually need suﬃciently ﬁne resolution.
Method 2. Decomposed local filters
Consider an undirected graph G =
( V, E ), | V | = n .
A graph convolution layer maps from input node features X ( u (cid:48) , c (cid:48) ) to output Y ( u, c ), where u, u (cid:48)
∈ V , c (cid:48) ∈
[ C (cid:48) ] ( c ∈ [ C ]) is the input (output) channel index, the notation [ m ] means { 1 , · · · , m } , and
[Equation]
The spatial and spectral graph convolutions correspond to diﬀerent ways of specifying M , c.f.
Sec.
The proposed graph convolution is deﬁned as
[Equation]
where B k ( u (cid:48) , u ) is non-zero only when u (cid:48) ∈
N ( d k ) u [Equation] d ) u denoting the d -th order neighborhood of u (i.e., the set of d -neighbors of u ), and K is a ﬁxed number.
In other words, B k ’s are
K basis of local ﬁlters around each u , and the order d k can diﬀer with 1 ≤ k ≤ K .
Both a k and B k are trainable, so the number of parameters are K · CC (cid:48)
+ (cid:80)
Kk =1 (cid:80) u ∈ V | N ( d k ) u | ∼
K · CC (cid:48)
+ Knp , where p stands for the average local patch size.
In our experiments we use
K up to 5, and d k up to 3.
We provide the matrix notation of (2) in Appendix A.1.
The construction (2) can be used as a layer type in larger GNN architectures.
Pooling of graphs can be added between layers, and see Appendix C.5 for further discussion on multiscale model.
The choice of K and neighborhood orders ( d 1 , · · · , d K ) can also be adjusted accordingly.
The model may be extended in several ways to be discussed in the last section.
2. Regularization by local graph Laplacian
The proposed L3Net layer enlarges the model capacity by allowing K basis ﬁlters at each location, and a natural way to regularize the trainable ﬁlters is by the graph geometry,
[Figure]
where, by construction, only the local graph patch is concerned.
We introduce the following regularization penalty of the basis ﬁlters B k ’s as
[Equation]
where L ( k ) u , equaling ( D − A ) restricted to the subgraph on N ( d k ) u , is the Dirichlet local graph Laplacian on N ( d k ) u (Chung & Graham, 1997)
(Fig. 2).
The training objective is
[Equation]
where L is the classiﬁcation loss.
As L encourages the diversity of B k ’s, the K -rankness usually remains a tight constraint in training, unless λ is very large, see also Proposition 3.
2. A unified framework for graph convolutions
Graph convolutions basically fall into two categories, the spatial and spectral constructions (Wu et al., 2020).
The proposed L3Net belongs to spatial construction, and here we show that the model (2) is a uniﬁed framework for various graph convolutions, both spatial and spectral.
Details and proofs are given in Appendix A.
• ChebNet (Deﬀerrard et al., 2016), GAT (Veliˇckovi´c et al., 2017), EdgeNet (Isuﬁ et al., 2020):
In ChebNet, M per ( c (cid:48) , c ) equals a degree-( L -1) polynomial of the graph Laplacian matrix, where the polynomial coeﬃcients are trainable.
GCN ( Kipf & Welling can be viewed as ChebNet with polynomial degree-1 and tied coeﬃcients.
The attention mechanism in GAT enhances the model expressiveness by incorporating adaptive kernel-based nonnegative aﬃnities.
In EdgeNet, the graph convolution operator is the product of trainable local ﬁlters supported on order-1 neighborhoods.
We have the following proposition:
Proposition 1.
L3Net (2) includes the following models as special cases:
(1) ChebNet (GCN) when K ≥ L ( K ≥ 2 ), L being the polynomial degree.
(2) GAT when K ≥ R ,
R being the number of attention branches.
(3) EdgeNet when K ≥ L , L
being the order of graph convolutions.
• CNN: When nodes lie on a geometrical domain that allows translation ( u (cid:48) − u ), in (2) setting B k ( u (cid:48) , u ) =
b k ( u (cid:48) − u ) for some b k ( · ) enforces spatial convolutional.
The convolutional kernel can be decomposed as (cid:80) k a k ( c (cid:48) , c ) b k ( · ) (Qiu et al., 2018).
Extension to CNN on manifold
mesh
is also possible as in
Masci
et al.
; Fey
et al.
We have the following:
Proposition 2.
Mesh-based geometrical CNN’s deﬁned by linear patch operators, including standard CNN on R d , and with low-rank decomposed ﬁlters are special cases of L3Net (2) .
We also note that L3Net reduces from locally connected GNN (Coates & Ng, 2011; Bruna et al., 2013), the largest class of spatial GNN, only by the low-rankness imposed by a small number of K in (2).
Locally connected GNN can be viewed as (1) with the requirement that for each ( c, c (cid:48) ), M ( u (cid:48) , u ; c (cid:48) , c ) is nonzero only when u (cid:48) is locally connected in u .
The complexities of the various models are summarized in Fig.
2 (Table), where L3Net reduces from the np · CC (cid:48) complexity of locally-connected net to be the additive ( np + CC (cid:48) )
times K .
When the number of channels C , C (cid:48) are large, e.g. in deep layers they ∼ 10 2 , and the graph size is not large, e.g., in landmark data applications np (cid:28) CC (cid:48) , the complexity is dominated by KCC (cid:48) which is comparable with ChebNet (GAT) if K ≈ L ( R ).
The computational cost is also comparable, as shown in experiments in Sec. 4.
Furthermore, we have:
Proposition 3.
Suppose the subgraphs on N ( d k ) u are all connected, given α u,k > 0 for all u, k , the minimum of (3) with constraint (cid:107) b ( k ) u (cid:107) 2 ≥ α u,k is achieved when b ( k ) u equals the ﬁrst Dirichlet eigenvector on N ( d k ) u , which does not change sign on N ( d k ) u .
The proposition shows that in the strong regularization limit of λ → ∞ in (4), L3Net reduces to be ChebNet-like.
The constraint with constants α u,k is included because otherwise the minimizer will be B k all zero.
The ﬁrst Dirichlet eigenvector is envelope-like (Fig. 2), and then B k ( · , u ) will be averaging operators on the local patch.
Thus the regularization parameter λ can be viewed as trading-oﬀ between the more expressiveness in the learnable
B k , and the more stability of the averaging local ﬁlters, similar to ChebNet and GCN.
Analysis
We analyze the representation expressiveness and stability (deﬁned in below) of the proposed
L3Net model.
All proofs in Appendix A, and experimental details in Appendix B.
3. Representation expressiveness of graph signals
The theoretical question of graph signal representation expressiveness concerns the ability for GNN deep features to distinguish graph signals.
While related, the problem diﬀers from the graph isomorphism test problem which has been intensively studied in the GNN expressiveness literature.
Here we prove that L3Net is strictly more expressive than certain spectral GNNs, and support the theoretical prediction by experiments.
We have shown that the L3Net model contains ChebNet (Proposition 1), and the following proposition proves the strictly more expressiveness for graph signal classiﬁcation.
We call
B a graph local ﬁlter if B ( u, v ) is non-zero only when v is in the neighborhood of u .
In a spectral GNN, the graph convolution takes the form as x (cid:55)→ f ( A ) x where f is a function on R , and A is the (possibly normalized) adjacency matrix.
Proposition 4.
There is a graph and 1)
A local ﬁlter B on it such that B cannot be expressed by any spectral graph convolution, but can be expressed by L3Net with K = 1 .
2) Two data distributions on the graph (two classes) such that, with a permutation group invariant operator in the last layer, the deep feature of any spectral GNN cannot distinguish the two classes, but that of L3Net with 1 layer and K = 1 can.
The fundamental argument is that spectral GNN is permutation equivariant (see e.g. Gama et al.
(2019), reproduced as Lemma A.1), and the local ﬁlters in L3Net break such symmetry to obtain more discriminative power.
The constructive example used in the proof is on a ring graph (Fig.
A.1, A and the basis B ), and the two data distributions shown in Fig.
Proposition 4 gives that, on the ring graph and using GNN with a global pooling in the last layer, an L3Net layer with K = 1 can have classiﬁcation power while a ChebNet with any order cannot.
On a chain graph (removing the connection between two end points in a ring graph), which not exactly follows the theory assumption, since the two graphs only diﬀer at one edge, we expect that it will remain a diﬃcult case for the ChebNet but not for
L3Net.
To verify the theory, we conduct experiments using a two-layer GNN and the results are in Fig.
3 (table).
In the last row, we further impose shared basis across nodes which reduces L3Net to a 1D convolutional layer, and the learned basis shows a “diﬀerence” shape (right plot) which explains its classiﬁcation power.
Results are similar using a 1-layer GNN (Tab. A.1).
The argument in Proposition 4 extends to other graphs and network types.
Generally, when a GNN based on global graph adjacency or Laplacian matrix applies linear combinations of local averaging ﬁlters, then certain graph ﬁlters may be diﬃcult to express.
[Figure] [Table]
We experimentally examine GAT, WLN and MPNN, which underperform on the binary classiﬁcation task, as shown in Fig.
3 (table).
3. Representation stability
We derive perturbation bounds of GNN feature representation, which is important for robustness against data noise.
The analysis implies a trade-oﬀ between de-noising and keeping high-frequency information, which is consistent with experimental observation in Sec. 4.
Consider the change in the GNN layer output Y deﬁned in (1)(2) when the input X changes.
For simplicity, let C = C (cid:48)
= 1, and the argument extends.
For any graph signal x : V → R and V (cid:48)
⊂ V , deﬁne (cid:107) x (cid:107) 2 ,V (cid:48) :
= ( (cid:80) u ∈ V (cid:48)
x ( u ) 2 ) 1 / 2 and (cid:104) x, y (cid:105) V (cid:48) =
(cid:80) u ∈ V (cid:48) x ( u )
y ( u ).
The following perturbation bound holds for the L3Net layer with/without regularization.
Theorem 1.
Suppose that X =
{ X ( u ) } u ∈ V is perturbed to be ˜ X
= X + ∆ X , the activation function σ : R → R is non-expansive, and sup u ∈ V (cid:80)
Kk =
| N ( d k ) u | ≤
Kp , then the change in the output { Y ( u ) } u ∈ V in 2 -norm is bounded by
[Equation]
Note that p indicates the averaged size of the d k -order local neighborhoods.
The proposition implies that when K is O (1), and the local basis B k ’s have O (1) 2-norms on all local parches uniformly bounded by β (1) , then the Lipschitz constant of the GNN layer mapping is O (1), i.e., the product of (cid:107) a (cid:107) 2 , β (1) and √ Kp , which does not scale with n .
This resembles the generalizes the 2-norm of a convolutional operator which only involves the norm of the convolutional kernel, which is possible due to the local receptive ﬁelds in the spatial construction of L3Net.
The local graph regularization introduced in Sec.
2.2 improves the stability of Y w.r.t.
∆ X by suppressing the response to local high-frequency perturbations in ∆ X .
Speciﬁcally, the local graph Laplacian L ( k ) u on the subgraph on N ( d k ) u is positive deﬁnite whenever the subgraph is connected and not isolated from the whole graph.
We then deﬁne the weighted 2-norm on local patch (cid:107) x (cid:107) L ( k ) u := (cid:104) x, L ( k ) u x (cid:105) N ( dk ) u , and similarly (cid:107) x
(cid:107) ( L ( k ) u ) − 1 .
Theorem 2.
Notation and setting as in Theorem 1, if furtherly, all the subgraphs on N ( d k ) u are connected within itself and to the rest of the graph, and there is
ρ ≥ 0 s.t. ∀ u, k , (cid:107) ∆ X (cid:107) ( L ( k ) u ) − 1 ≤ ρ
[Equation]
∆ X (cid:107) 2 ,N ( dk ) u , then
[Equation]
L ( k ) u .
The bound improves from Theorem 1 when ρβ (2) <
β (1) , and regularizing by R =
(cid:80) u,k (cid:107) B k ( · , u ) (cid:107) 2 L ( k ) u leads to smaller β (2) .
Meanwhile, on each N ( d k ) u the Dirichlet eigenvalues increases 0 < λ 1 ≤ λ 2 · · · ≤ λ p u,k , p u,k := | N ( d k ) u | , thus weighting by λ − 1 l in (cid:107) · (cid:107) ( L ( k ) u ) −decreases the contribution from high-frequency eigenvectors.
As a result, ρ will be small if ∆ X contains a signiﬁcant high-frequency component on the local patch, e.g., additive
Gaussian noise or missing values.
Note that in the weighted 2-norm of ∆ X by ( L ( k ) u ) − 1 , only the relative amount of high-frequency component in ∆ X matters (because any constant normalization of L ( k ) u cancels in the product of ρ and β (2) ).
The beneﬁts of local graph regularization in presence of noise in graph data will be shown in experiments.
Experiment
We test the proposed L3Net model on several datasets.
4. Object recognition of data on spherical mesh
We ﬁrst classify data on a spherical mesh: sphere MNIST and sphere ModelNet-40, following the settings in literature.
Though regular mesh on sphere is not the primary application
[Table] [Table]
[Table]
scenario that motivates our model, we include the experiments to compare with benchmarks and test the eﬃciency of L3Net on such regular meshes.
Following UGSCNN (Jiang et al., 2019), we implement diﬀerent mesh resolution on a sphere, indicated by “mesh level” (Fig.
4), where number of nodes in diﬀerent levels can vary from 2562 (level 4) to 12 (level 0).
All the networks consist of three convolutional layers, see more details in Appendix C.1.
Using the original mesh level (4;3;2), the ﬁnest resolution as in UGSCNN, L3Net gives among the best accuracies for sphere MNIST.
On Modelnet-40, L3Net achieves a testing accuracy of 90.24, outperforming ChebNet and GCN and and is comparable to UGSCNN which uses spherical mesh information (Tab. A.2).
When the mesh becomes coarser, as shown in Fig.(Table), L3Net improves over GCN and ChebNet ( L =4) and is comparable with UGSCNN under nearly all mesh settings.
We observe that in some settings ChebNet can beneﬁt from larger L , but the overall accuracy is still inferior to L3Net.
The most right two columns give two cases of coarse meshes where L3Net shows the most signiﬁcant advantage.
4. Facial expression recognition (FER)
We test on two FER datasets, Extended CohnKanade (CK+) (Lucey et al., 2010) and
FER13 (Goodfellow et al., 2013).
We use 15 facial landmarks, see Fig. 1, and pixel values on a patch around each landmark point as node features.
Details about dataset and model setup are in Appendix C.2.
Unlike spherical mesh, facial and body landmarks are coarse irregular grids where no clear pre-deﬁned mesh operation is applicable.
We benchmark L3Net with other GNN approaches, as shown in Table 1.
The local graph regularization strategy is applied on FER13, due to the severe outlier data of landmark detection caused by occlusion.
On CK+, L3Net leads all non-CNN models by a large margin, and the best model (1,1,2,3) uses comparable number of parameters with the best ChebNet ( L =4).
On
FER13, L3Net has lower performance than ChebNet and EdgeNet (Isuﬁ et al., 2020), but outperforms after adding regularization.
The running times of best ChebNet and L3Net models are comparable, and are much less than GAT’s.
4. Action recognition
We test on two skeleton-based action recognition datasets, NTU-RGB+D (Shahroudy et al., 2016) and Kinetics-Motion (Kay et al., 2017).
The irregular mesh is the 18/25-point body landmarks, with graph edges deﬁned by body joints, shown in Fig.
1 and Fig.
A.2.
We adopt ST-GCN (Yan et al., 2018) as the base architecture, and substitute the GCN layer with new L3Net layer, called ST-L3Net .
On Kinetics-Motion, we adopt the regularization
[Table] [Table]
mechanism to overcome the severe data missing caused by camera out-of-view.
See more experimental details in Appendix C.3.
We benchmark performance with ST-GCN (Yan et al., 2018), ST-GCN (our implementation without using geometric information) and STChebNet (replacing GCN with ChebNet layer), shown in Table 2.
L3Net shows signiﬁcant advantages on two NTU tasks, cross-view and cross-subject settings.
On Kinetics-Motion,
L3Net regains superiority over other models after applying regularization.
The results in both Table 1 and 2 indicate that stronger regularization sacriﬁces expressiveness for clean data and gains stability for noisy data, which is consistent with the theory in Sec. 3.2.
4. Robustness to graph noise
To examine the robustness to graph noise, we experiment on down-sampled MNIST data on 2D regular grid with 4-nearest-neighbor graph.
With no noise, on 28 × 28 data (Tab.
A.3), 14 × 14 data (Tab.
A.4), and 7 × 7 data (Tab. 3 “original” column), the performance of
L3Net is comparable to ChebNet (Deﬀerrard et al., 2016) and EdgeNet (Isuﬁ et al., 2020) and better than other GNN methods.
We consider three types of noise, Gaussian noise added to the pixel value, missing nodes or equivalently missing value in image input, and permutation of the node indices, details in Appendix C.4.
The results of adding diﬀerent levels of gaussian noise and permutation noise are shown in Tab. 3, while results of adding missing value noise is provided in Appendix C.4.
The results show that our regularization scheme improves the robustness to all three types of graph noise, supporting the theory in
Sec. 3.2.
Speciﬁcally, L3Net without regularization may underperform than ChebNet, but catches up after adding regularization, which is consistent with Proposition 3.
[Table]
Conclusion and Discussion
The paper proposes a new graph convolution model using learnable local ﬁlters decomposed over a small number of basis.
Strengths:
Provable enhancement of model expressiveness with signiﬁcantly reduced model complexity from locally connected GNN.
Improved stability and robustness via local graph regularization, supported by theory.
Plug-and-play layer type, suitable for GNN graph signal classiﬁcation problems on relatively unchanging small underlying graphs, like face/body landmark data in FER and action recognition applications.
Limitations and extensions: (1) Scalability to larger graph.
When | V | = n is large, the complexity increase in the npK term would be signiﬁcant.
The issue in practice can be remedied by mixing use of layer types, e.g., only adopting L3Net layers in upper levels of mesh which are of reduced size.
(2) Dynamically changing underlying graph across samples.
For more severe changes of the underlying graph, we can beneﬁt from solutions such as node registration or other preprocessing techniques, possibly by another neural network.
Related is the question of reducing model dependence on graph topology, possibly under a statistical model of the underlying graphs.
This includes transferability to larger networks.
(3) Incorporation of edge features.
Edge features can be transformed into extra channels of node features by an additional layer in the bottom, and the low-rank graph operation can be similarly employed there.
(4) Theoretically, the representation robustness analysis is to be extended to more general types of graph perturbation.
Generally, one can work to extend to other types of graph data and tasks.
Appendix A Proofs A. Details and proofs in Sec. 2.
To facilitate comparison with literature we provide a summary of various graph convolution models in matrix notation, the precise deﬁnition of which will be detailed in below.
For simplicity, only the linear transform part is shown and the addition of bias and point-wise non-linearity are omitted.
Notation as in Section 2.1, suppose X ∈ R n × C (cid:48) is the input node feature and Y ∈ R n × C the output feature,
• L3Net (ours): Y = (cid:80)
Kk =1 B k XA k , where B k ∈ R n × n is the local basis ﬁlter, and A k ∈ R C (cid:48)
× C are the coeﬃcients, both B k and A k are learnable.
• ChebNet/GCN: Y = (cid:80) L −
1 l =0 T l (˜ L ) X Θ l , where T l ( · )’s are Chebshev polynomials, ˜ L is the rescaled and re-centered graph Laplacian, T l (˜ L
) ∈ R n × n , and Θ l ∈ R C (cid:48)
× C are trainable.
• GAT: Y = (cid:80)
Rr =1 A ( r ) X Θ r , where A ( r ) ∈ R n × n is the graph attention aﬃnity computed adaptively from input features, and Θ r ∈ R C (cid:48)
× C are trainable and weightshared with the parameters in A ( r ) , see more in below.
• EdgeNet: Y = (cid:80)
L − 1 r =
P r X Θ r , where P r = (cid:81) rk =
Φ k for a sequence of trainable local ﬁlters Φ k , and Θ r ∈ R C (cid:48)
× C are trainable.
From the matrix formulation, it can be seen that when B k are the classical graph ﬁltering operators, e.g. polynomials of ˜ L , and A k the trainable Θ k , L3Net recovers the above graph convolution models in literature (c.f. Proposition 1).
In below we give more details, as well as the reduction to ﬁlter-decomposed CNN (c.f. Proposition 2).
A.1. Locally connected GNN
Speciﬁcally, the construction in Coates & Ng (2011); Bruna et al.
(2013) assumes that u and u (cid:48) belongs to the graph of diﬀerent scales, u (cid:48) is on the ﬁne graph, and u is on a coarse-grained layer produced by clustering of indices of the graph of the input layer.
If one generalize the construction to allow over-lapping of the receptive ﬁelds, and assume no pooling or coarse-graining of the graph, then the non-zero parameters are of the number (cid:88)
[Equation]
where n = | V | , p is the average patch size | N u | , and C and C (cid:48) are the number of input and output feature channels.
A.1. ChebNet/GCN, GAT and EdgeNet
• ChebNet/GCN
In view of (1), ChebNet (Deﬀerrard et al., 2016) makes use of the graph adjacency matrix to construct M .
Speciﬁcally, A sym :
= D − 1 / 2 AD − 1 / 2 is the symmetrized graph adjacency matrix (possibly including self-edge, then A equals original A plus I ), and L sym :
= I −
A sym has spectral decomposition L sym =
ΨΛΨ T .
Let ˜ L
=
α 1 I + α 2 L sym be the rescaled and re-centered graph Laplacian such that the eigenvalues are between [ − 1 , 1], α 1 , α 2 ﬁxed constants.
Then, written in n -by- n matrix form,
[Equation]
[Equation]
where T l ( · ) is Chebshev polynomial of degree l .
As A sym and then ˜ L are given by the graph, only θ l ’s are trainable, thus the number of parameters are
[Equation]
GCN (Kipf & Welling, 2016) is a special case of ChebNet.
Take L = 2 in (5), and tie the choice of θ 0 and θ 1 ,
[Equation]
where θ ( c (cid:48) , c ) is trainable.
This factorized form leads to the linear part of the layer-wise mapping as Y =
˜
AX Θ written in matrix form, where ˜ A is n -by- n matrix deﬁned as above, X ( Y ) is n -by- C (cid:48)
(- C ) array, Θ is C (cid:48)
-by- C matrix.
The model complexity is CC (cid:48) which are the parameters in Θ. • GAT
In GAT (Veliˇckovi´c et al., 2017), R being the number of attention heads, the graph convolution operator in one GNN layer can be written as (omitting bias and non-linear mapping)
Y
[Equation]
where { W ( r ) , a ( r ) } are the trainable parametrization of attention graph aﬃnity mechanism
A ( r ) , which constructs non-negative aﬃnities between graph nodes u and v adaptively from the input graph node feature X .
In particular, A ( r ) shares sparsity pattern as the graph topology, that is, A ( r ) ( u, u (cid:48) )
(cid:54) =
only when u (cid:48) ∈ N (1) u .
In the original GAT, Θ r = W ( r ) C ( r ) , where C ( r ) ’s are ﬁxed matrices such that the output from r -th head is concatenated into the output Y across r = 1 , · · · , R .
Variants of GAT adopt channel mixing across heads, e.g. a generalization of GAT in Isuﬁ et al.
(2020) uses extra trainable Θ r in (6) independent from W ( k ) .
Isuﬁ et al.
(2020) also proposed higherorder GAT by considering powers of the aﬃnity matrix
A ( r ) as well as the edge-varying version (c.f. Eqn.
(36)(39) in Isuﬁ et al.
As this higher-order GAT and the edge-varying counterpart are special cases of the edgy-varying GNN, we cover this case in
Proposition 1 3).
The model complexity of GAT:
In the original GAT where Θ r is tied with W ( r ) , the number of parameters in one layer is R ( C 0 C (cid:48) + 2 C 0 ), where R is the number of attention heads,
C = C 0 R , and W ( r ) :
R C (cid:48)
→ R C 0 .
When Θ r are free from { W ( r ) , a ( r ) } in (6), the number of parameters is R ( CC (cid:48)
+ C 0 C (cid:48)
+ 2 C 0 )
≤ R (2 CC (cid:48) + 2 C ), where W ( r ) maps to dimension
C 0 and Θ r maps to dimension C .
• EdgeNet
(Edge-varying GCN)
Per Eqn.
(1)(8) in Isuﬁ et al.
(2020), the edge-varying GNN layer mapping can be written as
[Equation]
where Φ 0 is an n -by- n diagonal matrix, and Φ k , k = 1 , · · · , r , are supported on N (1) u of each node u .
The trainable parameters are { Φ k } Rk =0 and { Θ r } Rr =0 , Θ r : R C (cid:48)
→ R C .
Edgevarying GAT implements polynomials of averaging ﬁlters, and general edge-varying GNN takes product of arbitrary 1-order ﬁlters.
The proof shows that EdgeNet layer is a special case of L3Net layer, while restricting B k to be of the product form (9) rather than freely supported on N ( d k ) u for user-speciﬁed order ( d 1 , · · · , d K ) is a non-trivial restriction.
The trainable parameters: Θ r has LCC (cid:48) many, Φ 0 has n , and Φ k , k = 1 , · · · , L − 1 each has np (1) many, p (1) being the average size o 1-neighborhood of nodes.
Thus the total number of parameters is
[Equation]
Proof of Proposition 1.
Part (1): Since GCN is a special case of ChebNet, it suﬃces to prove that (5) can be expressed in the form of L3Net (2) for some K .
By deﬁnition of ˜ L , mathematically equivalently,
[Equation]
where the coeﬃcients β l ’s are determined by θ l ’s, per ( c (cid:48) , c ).
Since A lsym propagates to the l -th order neighborhood of any node, setting B k ( u (cid:48) , u ) =
A k − 1 sym ( u (cid:48) , u ), B k ( u (cid:48) , u ) is non-zero
when u (cid:48) ∈ N ( k − 1) u , 1 ≤ k ≤ K :=
L , and then setting a k ( c (cid:48) , c ) =
β k − 1 ( c (cid:48) , c ) gives (5) in the form of (2).
Part (2): We consider (6) as the GAT model.
Recall that Θ r : R C (cid:48)
→ R C , then (6) can be re-written in the form of (1) by letting
[Equation]
which is a special case of (2) where R = K , A ( k )
=
B k and Θ k = a k .
Since A ( r ) ( u, u (cid:48) ) as a function of u (cid:48) is supported on u (cid:48)
∈ N (1) u , (6) belongs to the L3Net model (2)
where d 1 = · · · = d K = 1, in addition to that B k must be of the attention aﬃnity form, i.e. built from the attention coeﬃcients c ( r ) uv computed from input X via parameters { W ( r ) , a ( r ) } .
Part (3): Comparing with (1)(2), we have that (7) is a special case of L3Net (2) by letting
K = L ,
[Equation]
a k = Θ k − 1 , and d k = k − 1 for k = 1 , · · · , K .
A.1. Standard and geometrical CNN’s
Standard CNN on R d , e.g. d = 1 for audio signal and d = 2 for image data, applies a discretized convolution to the input data in each convolutional layer, which can be written as (omitting bias which is added per c , and the non-linear activation)
[Equation]
where U is a grid on R d .
We write in the way of “anti-convolution”, which has “ u (cid:48) − u ” rather than “ u − u (cid:48) ”, but the deﬁnition is equivalent.
For audio and image data, U is usually a regular mesh with evenly sampled grid points, and proper boundary conditions are applied when computing y ( u, c ) at a boundary grid point u .
E.g., boundary can be handled by standard padding as in CNN.
As the convolutional ﬁlters w c (cid:48) ,c are compactly supported, the summation of u (cid:48) is on a neighborhood of u .
More generally, CNN’s on non-Euclidean domains are constructed when spatial points are sampled on an irregular mesh in R d , e.g., a 2D surface in R 3 .
The generalization of (10) is by deﬁning the “patch operator” (Masci et al., 2015) which pushes a template ﬁlter w on a regular mesh on R d , d being the intrinsic dimensionality of the sampling domain, to the irregular mesh in the ambient space that have coordinates on local charts.
Speciﬁcally, for a mesh of 2D surface in 3D, d = 2, and w is a template convolutional ﬁlter on R 2 .
For any local cluster of 3D mesh points N u around a point u , the patch operator P u provides ( P u w )( u (cid:48) ) for u (cid:48) ∈ N u by certain interpolation scheme on the local chart.
The operator P u is linear in w , and possibly trainable.
As a result, in mesh-based geometrical CNN,
[Equation]
(11) and one can see that in Euclidean space taking ( P u w )( u (cid:48) )
= w ( u (cid:48) − u ) reduces (11) to the standard CNN as in (10).
In both (10) and (11), spatial low-rank decomposition of the ﬁlters w c (cid:48) ,c can be imposed (Qiu et al., 2018).
This introduces a set of bases { b k } k over space that linearly span the ﬁlters w c (cid:48) ,c .
For standard CNN in R d , b k are basis ﬁlters on R d , and for geometrical CNN, they are deﬁned on the reference domain in R d same as w c (cid:48) ,c , where d is the intrinsic dimension.
Suppose w c (cid:48) ,c = (cid:80) K k =1 β k, ( c (cid:48) ,c ) b k for coeﬃcients β k, ( c (cid:48) ,c ) , by linearity, (11) becomes
[Equation]
and similarly for (10).
The trainable parameters in (12) are β k, ( c (cid:48) ,c ) and the basis ﬁlters b k ’s, the former has KCC (cid:48) parameters, and the latter has (cid:80) k
p k , where p k is the size of the support of b k in R d .
Suppose the average size is p , then the number of parameters is Kp .
This gives the total number of parameters as
[Equation]
Proof of Proposition 2.
Since standard CNN is a special case of geometrical CNN 11, we only consider the latter.
Assuming low-rank ﬁlter decomposition, the convolutional mapping is (12).
Comparing to the GNN layer mapping deﬁned in (1), one sees that
[Equation]
which equals (2) if setting B k ( u (cid:48) , u )
=
( P u b k )( u (cid:48) ) and a k ( c (cid:48) , c ) =
β k, ( c (cid:48) ,c ) .
A.1. Strong regularization limit
Proof of Proposition 3.
The constrained minimization of R deﬁned in (3) separates for each u, k , and the minimization of b ( k ) u is given by min
[Equation]
For each u, k , the local Dirichlet graph Laplacian L ( k ) u has eigen-decomposition L ( k ) u
= Ψ ( k ) u Λ ( k ) u (Ψ ( k ) u ) T , where (Ψ ( k ) u ) T Ψ ( k ) u
= I , and the diagonal entries of Λ ( k ) u are eigenvalues of L ( k ) u , which are all ≥ 0 and sorted in increasing order.
By the variational property of eigenvalues, the minimizer of w in (13) is achieved when w = Ψ ( k ) u ( · , 1), i.e., the eigenvector associated with the smallest eigenvalue of L ( k ) u .
By that the local subgraph is connected, this smallest eigenvalue has single multiplicity, and the eigenvector is the Perron-Frobenius vector which does not change sign.
The claim holds for arbitrary α u,k > 0 since eigenvector is deﬁned up to a constant multiplication.
A. Proofs in Sec. 3.
Proof of Proposition 4.
Part 1): Let the graph be the ring graph with n nodes, and each node has 2 neighbors, n =8 as shown in Fig.
1 (right).
We index the nodes as u = 0 , . . .
, n −and allows addition/subtraction of u − v (mod n ).
Let B be the “diﬀerence” ﬁlter B ( u (cid:48) , u ) = 1 when u (cid:48)
=
u and − 1 when u (cid:48)
=
u + 1.
We show that B (cid:54) =
f ( A ) for any f , and in contrast, setting this B as the basis in (2) expresses the ﬁlter with K = 1.
To prove that B (cid:54) =
f ( A ) for any f , let π u be the permutation of the n nodes such that π u ( u + v ) =
( u − v ) for all v , i.e., mirror ﬂip the ring around the node u .
By construction, the graph topology of the ring graph is preserved under π u , that is, A π u := π u Aπ Tu =
A , whether A is the 0/1 value adjacency matrix or the symmetrically normalized one A sym =
D − 1 / 2 AD − 1 / 2 ( D is constant on diagonal) or other normalized version as long as the relation
A π u =
A holds.
By Lemma A.1 1), for any f : R → R ,
[Equation]
this means that if B = f ( A ) for some f , then Bπ u = π u B , which contradicts with the construction of B .
Part 2): Consider the two distributions of graph signals on the ring graph in 1), which we call “upwind/downwind” signals: X up consists of ﬁnite superpositions of functions on the ring graph which are periodic, smoothly increasing from 0 to 1 and then dropping to zero.
Signals in X up are under certain distribution, and X down consists of the signals that can be produced by mirror-ﬂipping the upwind signals.
That is, denoting x up ( x down ) an upwind (downwind) signal, π u the permutation as in 1) around any node u , then
[Equation]
where dist.
= means equaling in distribution.
Example signals of the two classes as illustrated in Fig.
Same as in 1), by construction A π u =
A .
Let F ( L ) be the mapping to the L -th layer spectral
GNN feature, for x up an upwind signal, Lemma A.1 2) gives that
[Equation]
The last layer applies group invariant operator U , then
[Equation]
this gives that
[Equation]
which means that the ﬁnal output deep feature via UF ( L )
[ A ] are statistically the same for the input signals from the two classes.
Meanwhile, the diﬀerence local ﬁlter B in the proof of 1) can extract feature to diﬀerentiate the two classes: with Relu activation function, the output feature after one convolutional layer and a global pooling, which is permutation invariant, can be made strictly positive for one class, and zero for the other class.
Thus, L3Net with 1 layer and 1 basis suﬃces to distinguish the X up and X down signals.
Lemma A.1 (Permutation equivariance, Proposition 1 in Gama et al.
Let A be the (possibly normalized) graph adjacency matrix, for any input signal x : V → R , and π ∈ S n a permutation of graph nodes,
The spectral graph convolution mapping f ( A ) satisﬁes that
[Equation]
2) Let F ( l )
[ A ] be the mapping to the l -th layer spectral GNN feature with graph adjacency A , then
[Equation]
Proof of Lemma A.1.
Proved in Gama et al.
and we reproduce with our notation for completeness.
Part 1): Denote the n -by- n permutation matrix also by π , then by deﬁnition, f ( A ) =
Uf (Λ) U T where A = U Λ U T is the diagonalization and U is orthogonal matrix, thus
[Equation]
and this proves 1).
Part 2): Each spectral GNN layer mapping adds the bias and the node-wise non-linear activation mapping to the graph convolution linear operator, which preserves the permutation equivariance.
Recursively applying to L layers proves 2).
A. Proofs in Sec. 3.
Proof of Theorem 1.
By deﬁnition,
[Equation]
then since σ is non-expansive, ∀ u ∈ V , |
[Equation]
Y
[Equation]
By that
[Equation]
[Equation]
15) we have that
[Equation]
u
[Equation]
and observe that
[Equation]
where we used the assumption on Kp to obtain the last ≤ .
Then (16) continues as
[Equation]
which proves that (cid:107) ∆
[Equation]
as claimed.
Proof of Theorem 2.
Same as in the proof of Theorem 1, we have (14).
The eigendecomposition L ( k ) u
= Ψ ( k ) u Λ ( k ) u (Ψ ( k ) u ) T has that (Ψ ( k ) u ) T Ψ
( k ) u
= I , and, under the connectivity condition of the subgraph, the diagonal entries of Λ ( k ) u all > 0.
Thus
[Equation]
which gives the Cauchy-Schwarz with weighted 2-norm as
[Equation]
Then similarly as in (16), using the deﬁnition of β (2) and the the condition with ρ , we obtain that
[Equation]
and the rest of the proof is the same, which gives that
[Equation]
which proves the claim
[Equation]
B Up/down-wind Classification Experiment B. Dataset Setup
We generate the Up/Down wind dataset on both ring graph and chain graph with 64 nodes.
Every node is assigned to a probability drawn from (0 , 1) uniform distribution.
Node with probability less than threshold = 0 .
1 will be assigned with a gaussian distribution with std = 1 . 5.
Each gaussian distribution added is masked half side.
Distribution masked left half is the ‘Down Wind’ class, distribution masked right half is the ‘Up Wind’ class, as shown in left plot in Fig.
We then sum up all half distributions from diﬀerent locations in each sample.
We generate 5000 training samples and 5000 testing samples.
B. Model architecture and training details
Network architectures.
• 2-gcn-layer model: GraphConv(1,32)-ReLU-MaxPool1d(2)-GraphConv(32,64)-ReLU-AvgPool(32)-FC(2), • 1-gcn-layer model: GraphConv(1,32)-ReLU-AvgPool(64)-FC(2), where GraphConv can be ChebNet or L3Net.
Training details.
We choose the Adam Optimizer, batch size of 100, set initial learning rate of 1 × 10 − 3 , make it decay by 0.1 at 80 epoch and train for 100 epochs.
B. Additional results
We report additional results using 1-gcn layer architecture in Tab.
A.1.
Our L3Net again shows stronger classiﬁcation performance than ChebNet.
[Table]
C Experimental Details C. Classification of sphere mesh data
Spherical mesh We conduct this experiment on icosahedral spherical mesh (Baumgardner & Frederickson, 1985).
Like S2CNN (Cohen et al., 2018), we project digit image onto surface of unit sphere, and follow Jiang et al.
by moving projected digit to equator, avoiding coordinate singularity at poles.
Here, we details the subdivision scheme of the icosahedral spherical mesh we used.
Start with an unit icosahedron, this sphere discretization progressively subdivide each face into four equal triangles, which makes this discretization uniform and accurate.
Plus, this scheme provides a natural downsampling strategy for networks, as it denotes the path for aggregating information from higher-level neighbor nodes to lower-level center node.
We adopt the following naming convention for diﬀerent mesh resolution: start with level-0( L 0) mesh(i.e., unit icosahedron), each level above is associated with a subdivision.
For level-
i ( L i ), properties of spherical mesh are:
[Equation]
in which N f , N e , N v denote number of edges, faces, and vertices.
To give a direct illustration of how many nodes each level of mesh has, we list them below,
• L 0 12 nodes • L 1 42 nodes • L 2 162 nodes • L 3 642 nodes • L 4 2562 nodes • L 5 10242 nodes
Network architectures We use a three-stage GNN model for this sphereMNIST, with each stage conduct convolution on spherical mesh of a speciﬁc level.
Detailed architecture (suppose mesh levels used are Li, Lj, Lk ):
Conv(1,16)
Li -BN-ReLU-DownSamp-ResBlock(16,16,64)
Lj -DownSampResBlock(64,64,256)
Lk -AvgPool-FC(10),
We use the 4-stage model architecture for SphereModelNet-40, where 4 mesh levels are:
L 5 , L 4 , L 3 , L 2.
Detailed architecture are:
Conv (6,32) L 5 -BN-ReLU-DownSamp-ResBlock(
,32,128) L 4 -DownSamp -ResBlock(128,128,512)
L 3 -DownSamp-ResBlock(512,512,2048)
L 2 -DownSamp-AvgPoolFC( , where the GraphConv(feat in, feat out) in above model architectures can be either Mesh
Convolution layer or Graph Convolution layer, and “ResBlock” is a bottleneck module with two 1 × 1 convolution layers and one GraphConv layer.
Training Details For SphereMNIST experiments, we use batch size of 64, Adam optimizer, initial learning rate of 0.01 which decays by 0.5 every 10 epochs.
We totally train model for 100 epochs.
For SphereModelNet-40 experiment, we batch size of 16, Adam optimizer, initial learning rate of 0.005 which decay by 0.7 every 25 epochs.
We totally train 300 epochs.
Results on ﬁne mesh
Tab.
A.2 show the results of SphereMNIST and Sphere-ModelNet40 on ﬁne meshes on the sphere.
Speciﬁcally, the mesh used for SphereMNIST here is of levels L 4 , L 3 , L 2, and the
SphereModelNet-40 mesh of levels L 5 , L 4 , L 3 , L 2, same as in Jiang et al.
[Table]
C. Facial Expression Recognition
Landmarks setting 15 landmarks are selected from the standard 68 facial landmarks deﬁned in AAM (Cootes et al., 2001), and edges are connected according to prior information of human face, e.g., nearby landmarks on the eye are connected, see Fig.
1 (left).
Dataset setup
CK+:
The CK+ dataset (Lucey et al., 2010) is the mostly used laboratory-controlled FER dataset (downloaded from: http://www.jeﬀcohn.net/resources/ ).
It contains 327 video sequences from 118 subjects with seven basic expression labels(anger, contempt, disgust, fear, happiness, sadness, and surprise).
Every sequence shows a shift from neutral face to the peak expression.
Following the commonly used ‘(static) image-based’ methods (Li & Deng, 2020), we extract the one to three frames in each expression sequence that have peak expression information in the CK+ dataset, and form a dataset with 981 image samples.
Every facial image is aligned and resized to (120 , 120) with face alignment model (Bulat & Tzimiropoulos, 2017), and then we use this model again to get facial landmarks.
As we describe in Sec.
4.2, we select 15 from 68 facial landmarks and build graph on them.
The input feature for each node is an image patch centered at the landmark with size (20 , 20), concatenated with the landmark’s coordinates, so the total input feature dimension is 402.
FER13: FER dataset (Goodfellow et al., 2013) is a large-scaled, unconstrained database collected automatically by Goole Image API (downloaded from: https://www.kaggle.com/c/challenges-in-representation-learning-facial-expressionrecognition-challenge/data ).
It contains 28,709 training images, validation images and 3589 test images of size (48 , 48) with seven common expression labels as CK+.
We align facial images, get facial landmarks, and select nodes & build graph the same way as we do in CK+.
Input features are local image patch centered at each landmark with size (8 , 8) and landmark’s coordinates, so the total input feature dimension is 66.
Network architectures.
• CK+: GraphConv(402,64)-BN-ReLU-GraphConv(64,128)-BN-ReLU-FC(7)
, • FER13: GraphConv(66,64)-BN-ReLU-GraphConv(64,128)-BN-ReLU-GraphConv(128,256)-BNReLU-FC(7), where GraphConv(feat in, feat out) here can be any type of graph convolution layer, including our L3Net.
Training details.
CK+: We use 10-fold cross validation as Ding et al.
Batch size is set as 16, learning rate is 001 which decay by 1 if validation
loss
remains same for last 15 epochs.
We choose Adam optimizer and train 100 epochs
for
each fold validation.
FER13:
We report results on test set.
Batch size is set as 32, learning rate is 0.0001 which decay 0.1 if validation loss remains same for last 20 epochs
We choose Adam optimizer and train models for 150 epochs
Runtime analysis details.
In section 4.2, we report the running time of our L3Net(order 1,1,2,3), 13.02ms, and best ChebNet, 12.56ms, on CK+ dataset, which are comparable.
Here, we provide more details about this.
The time we use to compare is the time of model ﬁnishing inference on validation set with batch size of 16.
For each model, we record all validation time usages in all folds and report the average of them.
The Runtime analysis is performed on a single NVIDIA TITAN V GPU.
C. Skeleton-based Action Recognition Dataset
setup
• NTU-RGB+D:
NTU-RGB+D (Shahroudy et al., 2016) is a large skeleton-based action recognition dataset with three-dimensional coordinates given to every body joint (downloaded from: http://rose1.ntu.edu.sg/datasets/requesterAdd.asp?DS=3 ).
It comprises 60 action classes and total 56,000 action clips.
Every clip is captured by three ﬁxed Kineticsv2 sensors in lab environment performed by one of 40 diﬀerent subjects.
Three sensors are set at same height but in diﬀerent horizontal views, − 45 ◦ , 0 ◦ , 45 ◦ .
There are 25 joints tracked, as shown in Fig.
A.2.
Two experiment setting are proposed by Shahroudy et al.
, cross-view (X-view) and cross-subject (X-sub).
X-view consists of 37,920 clips for training and 18960 for testing, where training clips are from sensor on 0 ◦ , 45 ◦ , testing clips from sensor on − 45 ◦ .
X-sub has 40,320 clips for training and 16,560 clips for testing, where training clips are fromsubjects, testing clips are from the other 20 subjects.
We test our model on both settings.
• Kinetics:
Kinetics (Kay et al., 2017) is a large and most commonly-used action recognition dataset with nearly 300, clips for
classes (downloaded from: https://deepmind.com/research/open-source/kinetics ).
We follow Yan et al.
(2018) to get point body joints from each frame using OpenPose (Cao et al., 2017) toolkit.
Input features for each joint to the Network is ( x, y, p ), in which x, y are 2D coordinates of the joint, and p is the conﬁdence for localizing the joint.
To eliminate the eﬀect of skeletonbased model’s inability to recognize objects in clips, we mainly focus on action classes that requires only body movements
Thus, we conduct our experiments on Kinetics-Motion, proposed by Yan et al.
This is a small dataset that contains 30 action classes strongly related to body motion.
Note that there are severe data missing problem in landmark coordinates in Kinetics data, so we also use our regularization scheme in this experiment
Network Architectures.
• NTU-RGB+D:
We follow the architecture in Yan et al.
(2018): STGraphConv(3,64,9,s1)-STGraphConv(64,64,9,s1)-STGraphConv(64,64,9,s1) STGraphConv(64,64,9,s1)-STGraphConv 64,128,9,s2)-STGraphConv 128,128,9,s1)- STGraphConv(128,128,9,s1)-STGraphConv(128,256,9,s2)-STGraphConv(256,256,9,s1)- STGraphConv(256,256,9,s1)-STAvgPool-fc(60).
• Kinetics:
We also design a computation-eﬃcient architecture for Kinetics-Motion with larger temporal downsampling rate, which results in less forward time:
STGraphConv(3,32,9,s2)-STGraphConv(32,64,9,s2)-STGraphConv(64,64,9,s1)-
STGraphConv(64,64,9,s1)-STGraphConv(64,128,9,s2)-STGraphConv(128,128,5,s1)-
STGraphConv(128,128,5,s1)-STGraphConv(128,256,5,s2)-STGraphConv(256,256,3,s1)-
STGraphConv(256,256,3,s1)-STAvgPool-fc(60), where the structure of STGraphConv(feat in, feat out, temporal kernel size, temporal stride) is:
GraphConv(feat in, feat out)-BN-ReLU-1DTemporalConv(feat out, feat out, temporal kernel size, temporal stride)-BN-ReLU. [Table]
[Table]
Training Details
• NTU-RGB+D:
We use batch size of 32, initial learning rate of 0.001 which decay by 0.1 at (30, 80) epoch, and total train 120 epochs.
SGD optimizer is selected.
We padding every sample temporally with 0 to 300 frames.
• Kinetics: We use batch size of 32, initial learning rate of 0.01 which decay by 0.1 at (40, 80) epoch, and total train 100 epochs.
SGD optimizer is selected.
We padding every sample temporally with 0 to 300 frames, and during training, we perform data augmentation by randomly choosing 150 contiguous frames.
C. Details of experiment on MNIST C.4. Simulated graph noise on 7 × 7 MNIST.
Here we describe three types of noise in our experiments:
Gaussian noise .
Given a 7 × 7 image from MNIST, we sample 49 values from N (0 , std 2 ).
The std controls the strength of noise added.
We conduct experiments under std = 0 .
3 as shown in Tab. 3.
The amount of noise is also measured by PNSR which is standard for image data.
Missing value noise.
Given a image, we randomly sample 49 values from U (0 , 1), and select nodes with probabilities less than a threshold.
This threshold is called noise level , which controls the percentage of nodes aﬀected.
Then, we remove the pixel value at those selected nodes.
Experiments with noise level = 0 .
3 are conducted.
Graph node permutation noise .
For each sample, we randomly select a permutation center node which has exact 4 neighbors.
Then, we rotate its neighbors clockwise by 90 degree, e.g., top neighbor becomes right neighbor, and then we update the indices of permuted nodes.
[Table]
C.4. Network architecture and training details
We use the same architecture for diﬀerent experiment settings:
GraphConv
[Equation]
32)-BN
[Equation]
ReLU
[Equation] BN [Equation] where GraphConv can be diﬀerent types of graph convolution layers.
We set batch size to 100, use Adam optimizer, and set initial learning rate to 1e-3.
Learning rate will drop by 10 if the least validation loss remains the same for the last 15 epochs.
We set total training epochs as 200.
We use 10,000 images for training.
We also adopt graph pooling layers in the above architecture:
GraphConv(1,32)-BN-ReLU-Graph Pooling-GraphConv(32, 64)-BN-ReLUGraph Pooling-FC(10).
More discussion about graph pooling layer and multi-scale graph convolution is detailed in
Appendix C.5.
C.4. Additional results
Here, we show experiments results on 28 × 28 , 14 × 14 grid, as well as 7 × 7 grid with missing values.
Tab.
A.3 shows results on 28 × 28 image grid.
Our model have better performance than other methods.
Tab.
A.4 shows results on 14 × 14 image grid, where our L3Net have comparable results with the best ChebNet (Deﬀerrard et al., 2016) method.
We shows our results on 7 × 7 image grid with missing values in Tab.
A.5.
With regularization, L3Net achieves the best performance in every experiment with diﬀerent noise levels.
C. Multi-scale graph convolution
The proposed L3Net graph convolution model (2) is compatible with graph down/upsampling schemes to achieve multi-scale feature extraction.
The graph down/up-sampling is usually implemented as a separate layer between graph convolution layers.
As an example, Fig.
A.3 illustrates three levels of graphs produced from an originally 14 × 14 image grid, denoted as G 1 , G 2 , G 3 , and they have 176, 45 and 9 nodes respectively.
On G 1 , 10% of pixels which contain the lowest amount of pixel intensities over the dataset are removed, and those nodes are located near the boundary of the canvas.
For each node x (cid:48)
i in the coarse-grained graph G 2 , a neighborhood consisting of nodes in
G 1 is constructed, called N ( x (cid:48)
i ; G 1 ).
A pooling operator computes the feature on x (cid:48)
i from those on N ( x (cid:48)
i ; G 1 ), and the pooled feature is used as the input to the graph convolution on G 2 .
A similar graph pooling layer is used from G 2 to G 3 .
The graph topology and local neighborhoods are determined by grid point locations.
Using a two-layer convolution with graph poolings in between from G 1 to G 3 , and the other setting same as in Table A.4, L3Net obtains 97 .
33 ± 0 .
15 test accuracy (basis order 1; 1; 2, with regularization 0.001).
We have also applied graph pooling layers on regular image grid on the 28 × 28 MNIST dataset.
The results, reported in Table A.3, show that multi-scale convolution in L3Net not only improves the classiﬁcation accuracy but also reduces the number of parameters.
Graph up-sampling layer can be used similarly.
These multi-scale approaches generally apply to graph convolution models, see, e.g., the hierarchical construction originally proposed for locally-connected GNN (Coates & Ng, 2011; Bruna et al., 2013).
There is also ﬂexibility in deﬁning the graph down/up-sampling schemes, and the choice depends on application.
An example of graph sampling operator on face mesh data is given in (Ranjan et al., 2018).
At last, apart from using separate down/up-sampling layers, it is also possible to extend the
L3Net model (2) to directly implement graph down/up-sampling, which would be similar to the convolution-with-stride (conv-t) operator in standard CNN.
Speciﬁcally, between G l and
G l +1 , the local basis ﬁlter B k ( u, u (cid:48) ) is deﬁned for u (cid:
) ∈
G l and u ∈ G l +1 , and B k ( u, u (cid:48) )
(cid:54)
=only when u (cid:48) is in a local neighborhood of u .
In matrix notation, B k is of size | G l +1 | -by- | G l | , and is sparse according to the graph local neighborhood relation between G l and G l +1 .