Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
Authors: Yaohua Wang
Yaobin Zhang
Fangyi Zhang
Senzhang Wang
Ming Lin
YuQi Zhang
Xiuyu Sun
Venue: ICLR 2022 Poster (2022)
Keywords: Face Clustering
Graph Convolutional Networks (GCN)
Computer Vision

Abstract: Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly according to $k$NN relations in the feature space, which may lead to a lot of noise edges connecting two faces of different classes. The face features will be polluted when messages pass along these noise edges, thus degrading the performance of GCNs. In this paper, a novel algorithm named Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine a proper number of edges connecting to each face image. It significantly reduces the noise edges while maintaining the good ones to build a graph with clean yet rich edges for GCNs to cluster faces. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods, proving its superiority and generalization. Code is available at https://github.com/damo-cv/Ada-NETS.

Published as a conference paper at ICLR 2022
ADA-NETS: FACE CLUSTERING VIA ADAPTIVE NEIGHBOUR DISCOVERY IN THE STRUCTURE SPACE
Yaohua Wang", Yaobin Zhang ", Fangyi Zhang, Ming Lin, YuQi Zhang
Alibaba Group
(xiachen wyh, zhangyaobin, zyb, zhiyuan. zfy, ming. 1, gongyou. zyq)
inc.com
Senzhang Wang
Xiuyu Sun
Central South University
Alibaba Group x ciuyyusxyeee baba-ince
szwang@csu edu.cr
ABSTRACT
Face clustering has attracted rising research interest recently to take advantage massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly according to kNN relations in the feature space, which may lead to lot of noise edges connecting two faces different classes. The face features will be polluted when messages pass along these noise edges, thus degrading the perfor- mance of GCNs. In this paper, a novel algorithm named Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine proper number of edges connecting to each face im- age. It significantly reduces the noise edges while maintaining the good ones build a graph with clean yet rich edges for GCNs to cluster faces. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-ee art methods, proving its superiority and generalization. Code is available at atttps: /ttp:::///gtttt/b// ctt. damo cv wwtpaaaaccocca--/-/Aaaa Ada-NETS
com
INTRODUCTION
The number of images on the web increases rapidly in recent years, a large portion of which are human-centred photos. Understanding and managing these photos with little human involvement demanding, such associating together photos from certain person. A fundamental problem towards these demands is face clustering (Driver & Kroeber, 1932).
Face clustering has been thoroughly investigated in recent years. Significant performance improve- ments (Wang et al. 2019b Yang et al. 2019: 2020 Guo et al. 2020 Shen et al.) 2021) have been obtained with Graph Convolutional Networks due their powerful feature propagation capacity. The representative DA-Net (Guo et al. 2020) and STAR-FC (Shen et al. 2021) use GCNs to learn enhanced feature embedding by vertices edges classification tasks to assist clustering.
However, the main problem restricting the power of existing GCN-based face clustering algorithms the existence of noise edges the face graphs. As shown in Figure (b), , noise edge means the connection between two faces of different classes. Unlike common graph datasets such as Citeseer, Cora and Pubmed with explicit link relation as edges (Kipf & Welling 2017). face images do not contain explicit structural information, but only deep features extracted from trained CNN model. Therefore, face images are treated as vertices, and the edges between face images are usually con- structed based on the kNN (Cover & Hart 1967) relations when building the graph: Each face serves
Equual contribution Corresponding author

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Published as a conference paper at ICLR 2022
[a] Face
(b) Noise edges naive KNN graph
(c) Edges built distance deep feature space may be Incorrect
(d) "One size-fits all solution conmect edges all vertices
Figure 1: The noise edges problem in GCN-based face clustering. Different shapes in figures rep- resent different classes. (a) Face images to be clustered. (b) Noise edges are introduced when con- structing graphs based on naïve &NN. (c) Connecting edges by feature distance may lead to noise edges. (d) The existing "One-size-fits-lll" solution using fixed number of neighbours for each ver- tex introduces many noise edges.
as probe to retrieve its & nearest neighbours by deep features (Wang et al. 2019b Yang et al. 2019 2020
Guo et al. 2020, Shen et al. 2021). The &NN relations are not always reliable because deep features not accurate enough. So the noise edges introduced to the graph along with the ANN The noise edges problem common in face clustering but has received little research attention. For example, the graph used in (Yang al., 2020 Shen al. 2021) contains about 38. 23% noise edges in testing. The noise edges will propagate noisy information between vertices, hurting their features when aggregation, and thus resulting in inferior performance. In Figure (b). the triangular vertex V is connected with three vertices of different classes, and will it be polluted by them with mes- sages passing in the graph. Therefore, GCN- -bsed linkage prediction cannot effectively address the noise edges problem in related works (Wang et al. 2019b Yang et al. 2020 Shen et al. 2021).
The challenges of removing the noise edges in the face graphs are two-fold as shown in Fig- ure [T) (c) (d). First, the representation ability of deep features is limited in real-world data. It is difficult to tell whether two vertices are of the same class solely according to their deep features, thus noise edges are inevitably brought by connecting two vertices of different classes. Second, it is hard to determine how many edges to connect for each vertex when building a graph: Too few edges connected will result in insufficient information aggregation in the graph. Too many edges con- nected will increase the number of noise edges, and the vertex feature will be polluted by wrongly connected vertices. Although Clusformer (Nguyen et al.) (021)) and GAT (Velickovic et al. 2018) try to reduce the impact the noise edges by the attention mechanism, the connections between various vertices are very complex, and thus it is difficult to find common patterns for the attention weight learning (Yang et al., 2020).
To overcome these tough challenges, the features around each vertex taken into account because they can provide more information. Specifically, each vertex feature representation can be improved when considering other vertices nearby. This is beneficial to address the representation challenge in Figure [ (c). Then, the number of edges between one vertex and others can be learned from feature patterns around instead of a manually designed parameter for all vertices. This learning method can effectively reduce the connection of noise edges which is crucial to address the second challenge in Figure (d). Based on the ideas above, a novel clustering algorithm, named Adaptive Neighbour discovEry in the struc Ture Space (Ada-NETS), is proposed to handle the noise edges problem for clustering. In Ada-NETS, a structure space is first presented in which vertices can obtain robust features by encoding more texture information after perceiving the data distribution. Then, a candidate neighbours quality criterion is carefully designed to guide building less-noisy yet rich edges, along with learnable adaptive filter leam this criterion. In this way, the neighbours of each vertex are adaptively discovered to build the graph with clean and rich edges. Finally, GCNs take this graph input cluster human faces.
The main contributions of this paper are summarized as follows:
To the best of our knowledge, this is the first paper to address the noise edges problem when building a graph for GCNs on face images. Simultaneously, this paper demonstrates its causes, great impact, weaknesses in existing solutions and the challenges to solve it.
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(1). Transfer Features to Structure Space
(UII). Clustering with Graph Convolutional Network
training
Graphh Comallatonal Ratwori
Adaptive Neighbour Discovery
21
-
testing
Faceimaga four classes
+ + probe
vareffering function
candidate neightous
disoovrrod nilggbours
Superision
The framework of Ada-NETS. (I). The features are transformed the structure space to obtain better similarity metrics. (II). The neighbours of each vertex are discovered by an adaptive filter. (III). A graph built with the neighbour relations discovered by (II) and the graph used by the GCN model to classify vertex pairs. The final clustering results are obtained using embeddings from GCNs to link vertex pairs with high similarities.
Figure 2:
The proposed Ada-NETS can alleviate the noise edges problem when building graph on face images, thus improve GCNs greatly to boost clustering performance.
State-of-the-aa performances are achieved on clustering tasks by Ada-NETS, surpassing the previous ones by a large margin on the face, person, and clothes datasets.
RELATED WORK
Face Clustering Face clustering tasks often face large-scale samples and complex data distribution, so it has attracted special research attention. Classic unsupervised methods are slow and cannot achieve good performances for their naive distribution assumptions, such as convex- convex-shaped shaped data in K-Means (Lloyd) 1982) and similar density of data in DBSCAN (Ester et al. 1996)). In recent years, GCN-based supervised methods are proved to be effective and efficient for face clustering. L-GCN (Wang et al. 2019b) deploys GCN for linkage prediction on subgraphs. DS-GCN (Yang) al. 2019) and VE-GCN Yang et al. 2020) both propose two-stage GCNs for clustering based on the big kNN graph. DA-Net (Guo al. 2020) conducts clustering by leveraging non-local context information through density-based graph. Clusformer (Nguyen et al.] 202T) clusters faces with transformer. STAR-FC (Shen al. 2021) develops structure structure-preserved sampling strategy to train the edge classification GCN. These achievements show the power GCNs in representation and clustering. However, the existing methods mostly build face graphs based kNN, which contain lot of noise edges. When building these graphs, the similarities hetween vertices are obtained only according to deep features which are not always accurate, and the number of edges for each vertex is fixed or determined by a similarity threshold.
Graph Convolutional Networks GCNs are proposed deal with non-Euclidean data and have shown their power in learning graph patterns. It is originally used for transductive semi-supervised learning Kiip & Welling 2017) and is extended inductive tasks by GraphSAGE (Hamilton al., 2017) that learns the feature aggregation principle. To further expand the representation power of GCNs, learnable edge weights are introduced the graph aggregation in the Graph Attention Network (GAT) (Velickovic et al. 2018). 8). In addition to face clustering, GCNs are also used in many tasks such as skeleton-basee
action recognition (Yan et al. 2018), knowledge graph (Schlichtkrull al. 2018) and recommend system (Ying et al. 2018). However, these methods are proposed on structure data, where the edges are explicitly given. The GCNs may not perform well on face image datasets if the graph is constructed with a lot of noise edges.
METHODOLOGY
Face clustering aims divide set of face samples into groups that samples in group belong to one identity, and any two samples different groups belong to different identities. Given a set of feature vectors V {V1, V2, mo Viy VN V; DR extracted from face images by trained
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Training
Testing
Lets
Adaptive Filter
ken
Adaptivo Filtrr
probo
negioous
candenennig bours 1lbbe
PerrIve candiir neighhour
Figure 3: Adaptive neighbour discovery process. offf is the extreme point of Q(i). In training phase, adaptive filter learns to fit kotl. In testing phase, adaptive filter estimates k and removes the candidate neighbours with orders beyond the predicted k
CNN model, the clustering task assigns group label for each vector vi. N is the total number of samples, and is the dimension of each feature. The Ada-NETS algorithm is proposed as shown in Figure to cluster faces by dealing with the noise edges in face graphs. Firstly, the features are transformed to the proposed structure space for an accurate similarity metric. Then an adaptive neighbour discovery strategy is used to find neighbours for each vertex. Based the discovery results, a graph with clean and rich edges is built as the input graph of GCNs for the final clustering.
3.1 STRUCTURE SPACE
The noise edges problem will lead the pollution of vertices features, degrading the performance of GCN-base clustering. It is difficull to determine whether two vertices belong to the same class just based on their deep features, because two vertices of different classes can also have a high similarity, thus introducing noise edges. Unfortunately, to the best of our knowledge, almost all existing methods (Wang et al.) 2019b; Yang et al., 20200 Guo et al.) 2020 Shen et al. (02II) build graphs only based on the pairwise cosine similarity between vertices using deep features. In fact, the similarity metric can be improved by considering the structure information, that is the neighbour- hood relationships between images of the dataset. Based this idea, the concept of the structure space is proposed to tackle this challenge. In the structure space, the features can encode more tex- ture information by perceiving the data distribution thus being more robust (Zhang al. 2020). A transforming function is deployed to convert one feature V; into the structure space, noted as vê:
+ (v.(V), Vi 21,2, N...
As shown in Figure 2 (I), with the help of the structure space, for one vertex V;, its similarities with other vertices are computed by the following steps: First, kNN of are obtained via an Approxi- mate Nearest-neighbour (ANN) algorithm based on the cosine similarity between v; and the other vertices, noted as (Vv, k) {Vi,, Viy, vi, J. Second, motivate by the kernel method (Shawe- Taylor & Cristianini 2004), instead of directly solving the form of 4p, we define the similarity of v; to each of its candidates in the structure space by
K (Vis Vi,) =
A (1 n) (v, Vs,)) + 1000 (vi, Vj {1,2, A,},
where v) weights the cosine similarity cos (vi, Vis) Jnc Vi)) that is inspired by the common-neighhour-base metric Zhong al. 2017). With the definitions above, & (v,, v.,) measures the similarity between v, and vi, in the structure space.
V.-V,, and the Jaccard similarity |
3.2 ADAPTIVE NEIGHBOUR DISCOVERY
The existing methods link edges from naive &NN relations retrieved by deep features (Wang et al. 2019b Yang et al., 2020 Shen et al. 2021) or using a fixed similarity threshold (Guo et al. 2020). These methods are all one- size-iize-ieee fits-al all solutions and the hyper-parameters have great impact on the performance. To address this issue, the adaptive neighbour discovery module proposed to learn from the features pattern around each vertex shown in Figure (II).
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For the vertex Vi, its candidate neighbours of size j the j nearest neighbour vertices based on the similarity of their deep features, where j = 1.2.... k. Its neighbours mean one specific sized candidate neighbours that satisfy some particular criterion described as follows. The edges between 0y and all its neighbours are constructed.
3.2.1 CANDIDATE NEIGHBOURS QUALITY CRITERION
Motivated by the vertex confidence estimation method (Yang et al. 2020), heuristic criterion is designed to assess the quality of candidate neighbours for each probe vertex. Good neighbours should be clean, i.e. most neighbours should have the same class label with the probe vertex, so that noise edges will not be included in large numbers when building graph. The neighbours should also be rich, so that the message can fully pass the graph. To satisfy the two principles, the criterion is proposed according to the Fg-score (Rijsbergen 1979) in information retrieval. Similar to visual grammars (Nguyen et al., (021). all candidate neighbours are ordered by the similarity with the prohe vertex in sequence. Given candidate neighbours of size probed by vertex vi, its quality criterion Q (j) is defined as:
Pr Rd
= =1 (1 82)
(3)
3r + Re
where Pr and Re are the precision and recall of the first j candidate neighbours with respect to the label of vs. 3 is a weight balancing precision and recall. A higher Q-value indicates better candidate neighbours quality.
3.2.2 ADAPTIVE FILTER
With the criterion above, off is defined as the heuristic ground truth value of the number of neigh- bours choose:
k off arg
max Q (j). jE
(4)
As shown in Figure the adaptive filter estimates kolf by finding the position of the high- est Q-value on the Q-value curve. The input the adaptive filter is the feature vectors
[va, Vi. Vi Vi, )rr E (k+++))x In training, given mini-batch with B sequences, adap-
tive filter is trained using the Huber loss Huber 1992):
Huber
Huber
2
(5)
1 122
E <<
off kg
where Huuher
C
{
8g
152
otherwise,
kgff
k, is the prediction koff and is an outlier threshold. With each prediction k off from k, the off
off
candidate neighbours with orders beyond k coll
are removed and the left will be treated as neighbours of the corresponding probe. The adaptive filter is implemented as a bi-directional LSTM (Schuster layers with shorteuts.
& Paliwal 1997 Graves & Schmidhuber 2005) and two
3.3 ADA-NETS FOR FACE CLUSTERING
To effectively address the noise edges problem in face clustering, Ada-NETS first takes advantage of the proposed structure space and adaptive neighbour discovery to build a graph with clean and rich edges. Then GCN model is used to complete the clustering in this graph shown in Figure (III) With similarity metric in the structure space and adaptive neighbour discovery method above, the discovered neighbours of vertex V, is denoted A VV,
NO (v., k) (v, Viy E NV Ind, < kk'
off
(6)
where Ind, means the index V., ranked by N. (v., Vi,) in the descending order. Based on these neighbour relations, undirected graph (F, A) is generated by linking edge between two
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vertices if any of which is the discovered neighbour of the other. F [v1 V2, 1.. /vvl" is the vertex feature matrix, and A is the adjacency matrix:
V; E Wv, v (v,,,, 0, otherwise.
ij
With the built graph 9 (F, A), A GCN model is used to learn whether two vertices belong to the same class. One GCN layer is defined as:
F -D-
(8)
where Ā A RNN is an identity matrix, D is the diagonal degree matrix that D; Σ A.,, F, and Wt are respectively the input feature matrix and weight matrix (-th layer, and (.)) is an activation function. Two GCN layers are used in this paper, followed with one FC layer with PReLU (He al. 2015) activation and one normalization. For a batch of randomly sampled vertices B,, the training loss is defined as variant version of Hinge loss (Rosasco et al. 2004):
Hinge = - + XCPoe
where Coos
1
vvavlt Cneg max [32 + Ues.gl+t 1,=1
Σ
11 14= 4
1
where Dv..y, is the cosine similarity of the GCN output features v', and v', of the two vertices v, and V, in the batch Bg. []- marr(0,)) -), 114 = =1yl is the number of the positive pairs, i.e., the ground ground-truth label 4 of v; and the ground-truth label I of v, are the same; 31 and Bg are the margins of the positive and negative losses, and is the weight balancing the two losses.
During inference, the whole graph of test data is input into GCN to obtain enhanced features F' ['2,,v',, "''''""""''''" E NXD' for all vertices, where D' is the dimension of each new feature. Pairs of vertices are linked when their similarity scores are larger than a predefined threshold 0. Finally, clustering is done by merging all links transitively with the union-find algorithm, i.e., wait- free parallel algorihmssAAnderon & Woll 199T).
EXPERIMENTS
4.1 EVALUATION METRICS, DATASETS, AND EXPERIMENTAL SETTINGS
The Signal-Noise Rate (SNR) and Q-value are used to directly evaluate the quality of graph building, where SNR is the ratio of the number correct edges and noise edges in the graph. BCubed F- score FB (Bagga & Baldwin 1998 Amigó et al. 2009) and Pairwise F-score Ep (Shi et al. 2018) are used to evaluate the final clustering performance.
Three datasets are used in the experiments: MS-Celeb-IM (Guo et al. 2016 Deng et al. 2019) is
a large-scale face dataset with about 5.8M face images of 86K identities after data cleaning. For a fair comparison, we follow the same protocol and features VE-GCN YYngg et al. 2019) divide the dataset evenly into ten parts by identities, and use part_0 as the training set and part_ to part_9 as the testing set. In addition the face data, Ada-NETS has also been evaluated for its potential in clustering other objects, The clothes dataset DecpFashion (Liu et al. 2016) is used with the same subset, split settings and features as VE-GCN (Yang al.) 2020), where there are 25.752 images of 3,997 categories for training, and 26,960 images of 3,984 categories for testing. MSMT17 (Wei al., 2018) is the current largest ReID dataset widely used (Chen et al. 202T: Jiang et al. 2021). Its images are captured under different weather, light conditions and time periods from 15 cameras, which are challenging for clustering. There are 32,621 1 images of 1,041 individuals for training and 93,820 images of 3,060 individuals for testing. Features are obtained from model trained on the training set (He et al. 2020).
The learning rate is initially 0.01 for training the adaptive filter and 0.1 for training the GCN with cosine annealing. 5 for Huber loss, 31 = 0.9, B2 = 1.0, À for Hingle loss and 8 = 0.5 for Q-value. The SGD optimizer with momentum 0.9 and weight decay le-5 is used. k is set 80, 5, 40 on MS-Celeb-IM, DeepFashion, and MSMT17. The experiments are conducted with
PyTorch (Paszke et al. 2019) and DGL. (Wang al. 2019a).

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Table Face clustering performance with different numbers of unlabeled images on MS-Celeb-IM.
#unlabeled
584K
1.74M
2.89M
4.05M
5.21M
Method
Fp
FB
Fp
FB
Fp
FB
Fp
FB
Fp
FB
K-Means
79.21
81.23
73.04
75.20
69.83
72.34
67.90
70.57
66.47
69.42
HAC
70.63
70.46
54.40
69.53
11.08
68.62
1.40
67.69
0.37
66.96
DBSCAN
67.93
67.17
63.41
66.53
52.50
66.26
45.24
44.87
44.94
44.74
L-GCN
78.68
84.37
75.83
81.61
74.29
80.11
73.70
79.33
72.99
78.60
DS-GCN
85.66
85.82
82.41
83.01
80.32
81.10
78.98
79.84
77.87
78.86
VE-GCN
87.93
86.09
84.04
82.84
82.10
81.24
80.45
80.09
79.30
79.25
Clusformer
88.20
87.17
84.60
84.05
82.79
82.30
81.03
80.51
79.91
79.95
DA-Net 90.60
STAR-FC 91.97
90.21
88.28
86.26
86. 17
84.13
84.70
82.63
83.46
81.47
Ada-NETS
92.79
91.40
89.33
87.98
87.50
86.03
85.40
84.48
83.99
83.28
Table 2: Clustering performance DeepFashion clothes dataset and MSMT17 person dataset.
Table 3: Contribution of the structure space (SS)
and adaptive neighbour discovery (AND).
DeepFashion
MSMT17
Method
Fp
FB
Fp
FB
K-Means
32.86
53.77
53.82
62.41
Method
Graph Building
Clustering
HAC
22.54
48.77
60.27
69.02
SS
AND
SNR
F'
FB
DBSCAN
25.07
53.23
35.69
42.32
Meanshift
31.61
56.73
49.22
60.06
1.62
77.17
77.25
Spectral
29.02
46.40
51.60
67.03
V
2.37
81.49
84.37
13.85
89.29
88.98
L-GCN
28.85
58.91
49.19
62.06
V
V
21.67
92.79
91.40
VE-GCN
38.47
60.06
50.27
64.56
STAR-FC
37.07
60.60
58.80
66.92
Ada-NETS
39.30
61.05
64.05
72.88
4.2
METHOD COMPARISON
Face clustering performance is evaluated on the MS-Celeb-1M dataset with different numbers of unlabeled images. Comparison approaches include classic clustering methods K-Means (Lloyd,
HAC (Sibson 1973). DBSCAN (Ester et al. 1996), and graph-based methods L-GCN (Wang 2019b). DS-GCN Yangg et al. 2019). VE-GCN YYng al., 2020p. DA-Net (Guo et al. 2020 Clusformer (Nguyen et al., 2021) and STAR-FC (Shen et al. 202T). In this section, to further enhance the clustering performance of GCNs, some noise added to the training graph. Results in [ that the proposed Ada-NETS reaches the best performance in all tests (0 0.96), outperforming STAR-FC by 1.19% to reach 91.40% BCubed F-score on 584K unlabeled data. With the increase in the number of unlabeled images, the proposed Ada-NETS keeps superior performance, revealing the significance of graph building in large-scale clustering.
1982)
To further evaluate the generalization ability of approach in non-face clustering tasks, compar- isons are also conducted on DeepFashion and MSMT17. As shown Table 2 Ada-NETS achieves the best performance in the clothes and person clustering tasks.
4.3 ABLATION STUDY
Study on the structure space and adaptive neighbour discovery tions of the structure space and adaptive neighbour discovery. The SNR of the built graph and the BCubed and Pairwise F-scores compared. It is observed that the structure space and adaptive neighbour discovery both contribute to the performance improvement, between which the adaptive neighbour discovery contributes more. With both components, the graph's 's SNR is largely enhanced
the contribu-

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gort
Figure 4:
Random examples of top 20 images ranked by the similarity with probe images.
Graph building and clustering perfor- mance of the quality criterion with different B.
Table 4:
Table Different estimation methods of the adaptive filter.
Graph Building
Clustering
Qhefore
Qufter
Fp
FB
Method
Target
Fp
FB
GAT
81.38
81.46
0.5
69.16
82.98
91.64
91.62
EQ
Q
86.10
87.61
Ori.
1.0
67.94
72.72
88.13
87.45
2.0
66.76
67.53
84.78
84.43
EQ
param
Q
59.56
68.05
Ek
of
92.55
91.63
0.5
69.16
88.96
95.51
94.93
Str.
1.0
67.94
77.18
94.54
93.97
Feg
of
92.79
91.40
2.0
66.76
69.38
94.16
93.14
by 13.38 times, and the clustering performance is also largely improved. Each line in Figure shows the discovery results with the first image the probe. ranked by the similarity in the struc- ture space. The images with a blue circle are of the same identity as the probe. They all successfully obtain greater similarity in the structure space than those with a red triangle that represents different identities. With the help of the adaptive filter, images after the yellow vertical line are filtered out, remaining clean and rich neighbours. Without the adaptive filter, the images with a red triangle will be connected with its probe, leading to pollution of the probe.
Study on the quality criterion According to Equation 3 the criterion contains hyper- hyper-parameter 3. Smaller 3 emphasizes more precision, and bigger 3 emphasizes three mostly-used values 3 {0.5, 2.0} to study how affects the neighbour discovery and clustering. Thole the performance of Ada-NETS under different /3 in the original (denoted as Ori.) and structure space (denoted as Str.). Qeefore and Qafter are the Q-value of candidate neighbours of size k and Roff Fp and FB the corresponding clustering performance under oofff It observed that Quiter is obviously improved compared with Qbelive in all circumstance. For the same 3, the improvements more obvious the structure space than in the original space. As analysed above, a higher Q-value indicates better candidate neighbours quality, e.g., more noise edges will be eliminated (clean) or more correct edges will be kept (rich) the graph. Therefore, the clustering performance in the structure space is also higher than in the original space as is expected. In addition, = 0.5 achieves the best clustering performance in both spaces while much less sensitive in the structure space to reach 95.51 % Pairwise F-score and 94.93% BCubed F-score.
recall. We choose
Study on the adaptive filter Adaptive filter proposed to select neighbours from candidate neigh- bours. Compared with the estimation method regress koll directly in the adaptive filter, noted as Ekgg some other estimation methods are also studied: Ek formulates the k estimation as a k- class classification task; EQ regresses Q-value for all j directly; Eparam fits Q-value with respect to with quadratic curve and estimates the parameters of this curve. Results Table show that EK and E that estimate ooff obtain obviously higher performance than EQ and EQ param that els Ek achieves close results but needs less parameters
estimate the Q-value. Compared with Ek
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90
(96)
85
Bruhed F-score
82
89
69
rtructure KNN
OFE
Threshald
65
Chain Sim
AND
Graph
GE + AND 55
40
BD
120
160
Number Candidate Neighbours
Faise Positive Rate (1e-4)
(c) Feature distribution vi- sualization for three types of features.
(a) Sensitivity to k of different graph building methods.
(b) ROC curves for one billion
randomly selected pairs.
Figure 5: (a) The sensitivity of clustering to k. Ada-NETS maintains a stable and outstanding performance. (b) The ROC curves on MS-Celeb-IM part.1. All embedded features have better ROC performances than the original feature embedding, and the graph embedding output by GCN (c) Feature distribution visualization for
with the help of AND and SS has the best performance. three types of features: original feature embedding (c.1), graph embedding of GCN with the adaptive neighbour discovery strategy the original space (c.2) and structure space (c.3).
to learn. GAT is also compared to eliminate noise edges by the attention mechanism, but does not obtain competitive results because of the complex feature pattern (Yang al. 2020).
Study on the sensitivity to ks Figure 5 (a) shows the clustering performance with the variance of k. "KNN" represents directly selecting the nearest k neighbours build the graph in existing methods (Yang et al.] 2020 Shen et al.] 2021). and "Structure kNN" represents selecting kNN in the structure space with A( 256). Despite the help in the structure space, the two ANN methods are all sensitive to & since more noise edges are included when & increases. The "Threshold" and "Chain Sim" method are also also sensitive the number of candidate neighbours and have not achieved good results. The details of each method can be found in Table in appendix. However, the proposed Ada-NETS can relatively steadily obtain good performance, showing that our method can effectively provide clean and rich neighbours to build the graph for GCNs.
Study on the graph embedding In Ada-NETS, GCN is used produce more compactly dis- tributed features, thus more suitable for clustering. ROC curves of one billion randomly selected pairs are shown in F the worst ROC performance, and the graph embedding (GE) output by the GCN is enhanced by large margin. With the help of the adaptive neighbour discovery (GE + AND), the output feature is more discriminating. When the discovery is applied in the structure space (GE + AND SS), GCN can output the most discriminating features. Distributions of embeddings after dimensionality re- duction via PCA (Pearson| 1901) of ten randomly selected identities are shown in Figure 5 (c). It is observed that the better ROC performance a kind of feature embedding has in Figure (b), the more compact its embeddings are for certain identity. With better features embeddings, the clustering can be started again. In Table of appendix. the clustering results on MS-Celeb- IM are compared with the ones using original feature embeddings. observed that the graph embeddings further enhance NETS. The details are described in Section A.8 of appendix.
It is observed that the original feature embedding (OFE) obtains
CONCLUSION
This paper presents a novel Ada-NETS algorithm to deal with the noise edges problem when build- ing the graph in GCN-based face clustering. In Ada-NETS, the features are first transformed to the structure space to enhance the accuracy of the similarity metrics. Then an adaptive neighbour dis- covery method is used to find neighbours for all samples adaptively with the guidance of heuristic quality criterion. Based on the discovered neighbour relations, a graph with clean and rich edges is built the input of GCNs to obtain state-of- of-teoofthheart the face, clothes, and person clustering tasks.
Acknowledgement This work was supported by Alibaba Group through Alibaba Innovative Re- search Program.

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A APPENDIX
In this appendix, present the supplementary information on 1) the calculations of the precision and recall; 2) the calculation of the Jaccard similarity; 3) the detailed description of different designs of the adaptive filter; 4) the baseline results of selecting neighbours by various thresholds based on the cosine similarity of deep features; 5) the baseline results of directly clustering on the built graph without GCNs; 6) the tmme-cosumiigg analysis of Ada-NETS.
A.1 PRECISION AND RECALL
In the paper, precision Py and recall Re are used to obtain the quality criterion Q in Equation 3. The calculations of PrJ and Re are illustrated by example in Figure 6 Given dataset that has 15 samples of three classes, assume that one probe sample belonging class is selected from the dataset. Samples are first ranked by their similarities to the probe. For candidate neighbours of size j (j 6), Pr and Re are and respectively, as calculated in the Figure
The dataset of 15 samples and
probe
Vi
similarity
A
Vi,
Viz
Vig
Vi,
Vis
Vis
candidate neighbours of size
class 1
class 2
class 3
Re Rc|1
10
11
Figure 6: The calculation of precision Py and recall Rel.
A.2 THE JACCARD SIMILARITY
In the paper, feature similarities calculated in the structure space as in Equation 2. This sim- ilarity includes the Jaccard similarity which is calculated based on the k-reciprocal nearest neigh- bours (Zhong et al. 2017). defined as
Juc (v. vi,) = ( k) k) (v,,, k)]
(10)
[ (v. k)l'
where (v, k) encodes the structure information of feature with variant version of k-reciprocal nearest neighbours:
RJ k) - k R(v, URR(, 1
(11)
k) 3 R( 1 *)), Vr R(v,k),
s.t. R(v,
where
k), Vr N(v,
(12)
With the definition above, data distribution can be perceived in the structure space to obtain more accurate feature similarities.
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A.3 DESIGNS OF ADAPTIVE FILTER
In the paper, four kinds of different designs for the adaptive filter are proposed and their perfor- mances are compared in Table Two methods estimate Q values, and two estimate kott directly. Table shows detailed descriptions of the four designs.
Table 6: Descriptions of four kinds of different designs of the adaptive filter.
Symbol
Method Description
EQ seg
Regress Q-values in Equation 3 in the paper for each j E {1,2, k). During inference, knff determined by Equation using predicted Q- values.
EQ
Use quadratic curve to fit the trends of Q() with respect to j. which then used to predict Q values to get koff as defined in Equation 4 during inference. The parameters of the quadratic curve is learned as targets this method.
param
Formulate the ks estimation as multi-class classification task. One class for each possible j value Equation 4 and use softmax loss in training. Regress directly the ground-truth kaff values as defined in Equation 4.
cls
Frgg
Ek
A.4 CLUSTERING WITH FIXED THRESHOLDS
In addition directly selecting the nearest & neighhours build the graph as in Figure (a), select- ing nearest neighbours by a fixed similarity threshold is also evaluated. In Table it is observed that the best performance is achieved when the similarity threshold is 0.60. The highest Pairwise and BCubed F-score are 87.59 and 87.13, which are much lower than 92.79 and 91 91.40 of Ada-NETS. This shows that the method of selecting neighbours by fixed threshold for all samples is not as good as the proposed method in Ada-NETS, which is adaptive for each sample.
Table 7: Beubed F-score of using various thresholds to select neighbours build graph.
Threshold
0.75
0.70
0.65
0.60
0.55
Fp
68.48
79.64
77.88 85.04
86.58
87.59
FB
67.74
78.26
87.13
79.43
A.5 CLUSTERING WITHOUT GCNS
When a graph with clean and rich edges is built after neighbour discovery, a naive graph-cut can be conducted the graph as the clustering baseline. The graph-cut method directly breaks the edges that have similarities lower than a predefined similarity threshold (which is tuned for the best), and GCNs used.
Table 8: Comparison of clustering performance of graph-cut (in the original space and structure space) and Ada-NETS MS-Celeb-IM part 1.
Method
Fp
FB
Graph-eut (Original)
72.95
76.22
Graph-cut (Structure)
82.36
85.09
Ada-NETS
92.79
91.40
In Table 3. the first two lines show the clustering performance of graph-cut in the original space and structure space. Compared with Ada-NETS, the first two methods have very poor performances. This shows that the GCN module important for clustering. In addition, it can be seen that the clus- tering performance using graph-cut in the structure space is better than in the original space, proving
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that vertices obtain robust features by encoding texture information after perceiving the data distribution the structure space.
A.6 CLUSTERING TIME-CONSUMING ANALYSIS
Empirically, Ada-NETS takes about 18.9 minutes to cluster part_1 test set (about 584k samples) on 54 E5-2682 v4 CPUs and 8 NVIDIA P100 cards. It is much faster than L-GCN (Wang al. 2019b) and LTC Yang et al. 2019) which take 86.8 minutes and 62.2 minutes, but slower than STAR-FC (Shen et al, 2021). DA-NET (Guo al., 2020) and VE-GCN (Yang et al., (020) which take about to 11 minutes. Many parts of Ada-NET'S can be easily parallelized, but it is out of the scope of this work. Although Ada-NETS is not the fastest, the time consumption is still within reasonable range.
A.7 COMPARISON WITH OTHER GRAPH BUILDING METHODS
In Figure 5 (a) the Beubed F-score of Ada-NETS is compared with four other graph building methods under different k value to show its superiority and robustness. Methods for comparison include: &NN, Structure &NN, Threshold, and Chain Sim. Table 9 shows detailed description of these graph building methods.
Table 9: Descriptions of differrnnt graph building methods.
Parameter Setting
Method Description
ANN
k is set at 10, 20, 40, 80 and 160.
Each vertex is connected with its nearest neighbours as in Yang et al.)
2019 2020).
Structure &NN
I is set at 10, 20, 40, 80 and 160.
Each vertex is connected with its nearest neighbours in the structure space proposed in the paper.
Threshold
Vary the threshold from 0.55 0.75 by step of 0.05. is calcu- lated as the mean of the neighbours for each vertex at the given thresh- old.
Choose neighbours whose cosine similarities to the probe vertex are smaller than a predefined threshold as in Guo et al. (2020).
Chain Sim
Vary the similarity threshold from 0.4 to 0.8 by step of 0.1. & is cal- culated the mean of the number neighbours for each vertex at the given threshold.
First calculate each vertex's den- sity, and then construct a chain by incrementally adding the ver- tices which have similarity scores higher than predefined threshold and also densities larger than the last one as in Guo al. (2020). Build the graph with the method proposed AdT-NETS.
Graph of Ada-NETS
The candidate neighbour size is set 10, 20, 40, 80 and 160.
A.8 ADA-NETS WITH BETTER FEATURES EMBEDDINGS
Table 10: Pairwise F-score of original feature embedding and graph embedding MSSCellb-IMM
Input Feature
584K
1.7M
2.9M
4.1M
5.2M
OFE GE+AND+SS
92.79
89.33
87.50
85.40
83.99
93.74
91.19
89.42
87.91
85.65
These discriminating graph embeddings of "GE AND + SS" used the input of Ada-NETS to get the final clustering results for enhancement (0 = 0.99). Table I0 the clustering results on MS-Celeb-IN IM are compared with the ones using original feature embeddings. It is observed that the
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graph embeddings further enhance Ada-NETS from -ate-offhhe-artt to remarkable 93.74% on Fp, achieving nearly 1% improvement on 584K unlabeled data again.
16


Reviews:
[{"review_id": "jEWoySflVY2", "reviewer": "Reviewer_DwFA", "summary": "", "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": "strengths:\n1. The paper is easy to follow;\n2. Experimental results are good.\nweaknesses:\n1. The novelty of the proposed method is limited. Both of the Eq. (2), Eq. (3) and the GCN structure are previously proposed, this paper just combines them to obtain better results;\n2. Since the task of this paper is clustering, which is an unsupervised task. However, but in Eq. (3), sample labels are used to calculate quality criterion Q, which is not clear.\n3. The adaptive neighbour discovery and GCN are two seperated processes in current propose method. Therefore, any other graph optimization method can be used before the GCN process. The authors should conduct experiments to validate the efficacy of the proposed adaptive graph construction.", "summary_of_the_paper": "In order to address the problem that face clustering is often degraded by noise edges in similarity graph, this paper proposes Ada-NETS by constructing clean graphs for GCNs. In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images. Then, an adaptive neighbour discovery strategy is proposed to determine a proper number of edges connecting to each face image. Experiments on multiple public clustering datasets show that Ada-NETS significantly outperforms current state-of-the-art methods.", "summary_of_the_review": "Novelty is limited and some motivations are not clear.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "ij7alRtmj6B", "reviewer": "Reviewer_FhxA", "summary": "", "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": "The paper provides a new perspective to boost the performance of GCN-based face clustering methods.\nIn summary, the novelty is good and experimental result seems promising.\nBut the experimental evaluation needs more explaination and  extension.", "summary_of_the_paper": "This paper proposes a novel algorithm named Ada-NETS to alleviate the noise edges problem when building a graph on face images, thus improve GCNs to boost face clustering performance. \nStrengths:\n1) This is the first paper to address the noise edges problem when building a graph for GCNs on face images.\n2) The proposed Ada-NETS achieves state-of-the-art performances on multiple public datasets. And the experimentation is vert sufficient.\nweaknesses:\nThe author chooses F-beta-score as the candidate neighbors quality criterion, which seems to be a little bit heuristic. It is expected to explain the motivation of this design and demonstrate its superiority over other quality criterions.", "summary_of_the_review": "The experimental evaluation is unsatisfatory.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "5B5rQwH2Xyk", "reviewer": "Reviewer_Q8yu", "summary": "", "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": "**StrongPoints**\n\n(1) In the reviewer's perspective, the proposed design is well organized to enhance the clustering performance.\n\n(2) The datasets, MS1M, deep-fashion, and MSMT17, used in the experiments are large and realistic. \n\n(3) The experimental results in Table 1 show the method's superiority compared to current state-of-the-art methods.\n\n(4) Table three to six provides analyses on each proposion of the paper, which is well organized.\n\n\n**WeakPoints**\n\nFirst, note that the reviewer is positive about this work, and this section is much like questions rather than criticism.\n\n(1) It seems that the results in Table 1 would be governed by the expression power of the face feature v. The reviewer might miss and thank the author in advance for the clarification of the feature extraction process for the experiment. Similar to the other datasets in the experiment.\n\n(2) Also, since it is possible to access face features with various representation powers, the reviewer wants to see the tendency between face recognition performance and face clustering. For example, if the user uses powerful recognition features or vice versa, how does the effect of the proposed contribution change? It would be nice to refer to the webpage here: https://github.com/deepinsight/insightface/tree/master/model_zoo.\n\n(3) Does the author have a plan to open the source code of the paper? It would be helpful for all the researchers in this field.\n\n\n**Overall**\n\nThe reviewer is positive about the paper. Unless some critical issues during the reviewing process occur (such as missing very important s-o-t-a methods), and if detailed comments of the authors are provided, the reviewer thinks the paper is worth publishing in this stage.\n\n\n**Post Rebuttal**\nThank you for the rebuttal. The reviewer is satisfied with the feature extraction process and experiments on features with various recognition performances. It seems that the numbers in the Tables show the feature robustness of the proposed Ada-NETS. Overall the reviewer is positive for the usefulness of the method and will keep the current rating (accept. Good paper).\n", "summary_of_the_paper": "This paper proposes a new face clustering algorithm that significantly improves the past state-of-the-art GCN, which generates many noise edges that are harmful to clustering performance. The paper focuses on the noise edges in face clustering fields and proposes Adaptive Neighbour discovEry in the strucTure Space (Ada-NETS). In Ada-NETS, a 'structure space' is presented to obtain robust face features by encoding more texture information reflecting the data distribution. Then, a candidate neighbor quality criterion is presented to build less-noisy edges, along with a learnable adaptive filter to learn this criterion. Finally, the algorithm's effectiveness is validated by measuring the performance on various data domains: face, person, and clothes, with a large margin.\n\nTechnically, \n\n(1) The paper strengthens the credibility of the edge between vertices by applying the Jaccard similarity of the two vertices proposed by Zhong et al., 2017, with widely used cosine similarity. \n\n(2) To design adaptive neighbor discovery, they first define a quality criterion reflecting the two quality factors: cleanness and information abundance (rich) of the graph.\n\n(3)  Using top-k quality criterion vertices as gt, adaptive filter defined by bidirectional lstm and fully connected layers is trained to predict a future quality estimate to remove or add the neighbors.", "summary_of_the_review": "See the section above.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "1pZaW-nhFbn", "reviewer": "Reviewer_MCAC", "summary": "", "questions": "", "limitations": "", "rating": 3, "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": "There a two main issues with the paper: The first one is the limited amount of technical contribution for a high quality venue like ICLR. The authors put a lot of existing techniques together to build their algorithm but overall the proposed combination as a whole does not constitute a significant and widely applicable contribution to the field that might be of interest to the ICLR community. Secondly, the application domain is primarily limited to faces which is very specific application domain. On the positive side the experiments show promising performance and ablation studies present interesting results. I believe that CVPR is a much better fit for this paper where the contribution and application domain will be much more appreciated by the corresponding community.\n\n**After Rebuttal:** I would like to thank the reviewers for their response. Unfortunately, I still believe that the paper's focus (face clustering) is too narrow for ICLR and that the novelty is limited as a number of existing techniques are put together to solve a very domain-specific problem.", "summary_of_the_paper": "This paper proposes a new way of constructing a graph for Graph Convolutional Networks (GCNs) for face clustering that is claimed to alleviate the problem of having a significant amount of noisy edges in the graph as in previous methods for face clustering. Their method consists of two innovations: firstly, facial features are transformed to a structured space which improevs the accuracy of facial similarity metrics. Secondly, an method which finds the neighbours of each face is proposed based on a heuristic quality criterion. Once the graph is built the authors apply existing algorithms for GCNs.", "summary_of_the_review": "A decent paper with good amount of novelty for a computer vision conference where faces are particularly studied but of limited novelty and interest for the ICLR community. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Meta-review: All reviewers agree that the presented ADA-Nets approach is very interesting and sufficiently novel, addressing the degradation problem in face clustering. The reviewers are satisfied with the presented experimental studies in most cases. The rebuttal addressed a large majority of additionally raised questions. I disagree with one reviewer’s comment – that the focus of the paper is too narrow – because clustering techniques are of great interest to the ICLR community. I believe that the paper will be of interest to the audience attending ICLR and would recommend a presentation of the work as a poster.
Decision: Accept (Poster)