Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
Authors: Yixiao Ge
Dapeng Chen
Hongsheng Li
Venue:  (2020)
Keywords: Label Refinery
Unsupervised Domain Adaptation
Person Re-identification

Abstract: Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-art unsupervised domain adaptation methods for person re-ID transferred the learned knowledge from the source domain by optimizing with pseudo labels created by clustering algorithms on the target domain. Although they achieved state-of-the-art performances, the inevitable label noise caused by the clustering procedure was ignored. Such noisy pseudo labels substantially hinders the model's capability on further improving feature representations on the target domain. In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner.  In addition, the common practice is to adopt both the classification loss and the triplet loss jointly for achieving optimal performances in person re-ID models. However, conventional triplet loss cannot work with softly refined labels. To solve this problem, a novel soft softmax-triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance. The proposed MMT framework achieves considerable improvements of 14.4%, 18.2%, 13.1% and 16.4% mAP on Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT unsupervised domain adaptation tasks.

Published as a conference paper at ICLR 2020
MUTUAL MEAN-TEACHING:
PSEUDO LABEL REFINERY FOR UNSUPERVISED Do- MAIN ADAPTATION ON PERSON RE-IDENTIFICATION
Yixiao Ge, Dapeng Chen Hongsheng Li The Chinese University of Hong Kong (yxge@ling) hali@ee]. cuhk. edu.hk hk
ABSTRACT
Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-ae unsupervised domain adaptation methods for per- son re-II) transferred the learned knowledge from the source domain by opti- mizing with pseudo labels created by clustering algorithms on the target domain. Although they achieved state-of-the-an performances, the inevitable label noise caused by the clustering procedure was ignored. Such noisy pseudo labels sub- stantially hinders the model's capability on further improving feature representa- tions on the target domain. In order to mitigate the effects of noisy pseudo labels, we propose softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual tures from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner. In addition, the com- mon practice to adopt both the classification loss and the triplet loss jointly for achieving optimal performances in person re-ID models. However, conven- tional triplet loss cannot work with softly refined labels. To solve this problem, a novel soft softmax- triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance. The pro- posed MMT framework achieves considerable improvements of 14.4% %, 18.2% 4 13.4% % and 16.4% mAP on Market-to-Dukee Duke-to-Market. MtrMMT and Buke-to-MSMT unsupervised domain adaptation tasks.
(MMT), to learn better fea-
INTRODUCTION
Person re-identification (re-ID) aims at retrieving the same persons' images from images captured by different cameras. In recent years, person re-ID datasets with increasing numbers of images were proposed to facilitate the research along this direction. All the datasets require time-consuming an- notations and are keys for re-ID performance improvements. However, even with such large-scale datasets, for person images from a new camera system, the person re-ID models trained on exist- ing datasets generally show evident performance drops because of the domain gaps. Unsupervised Domain Adaptation (UDA) is therefore proposed to adapt the model trained the source image do- main (dataset) with identity labels to the target image domain (dataset) with no identity annotations.
State-of-the-art UDA methods (Song et al., 2018 Zhang et al. 2019b Yang et al. 2019) for person re-ID group unannotated images with clustering algorithms and train the network with clustering- generated pseudo labels. Although the pseudo label generation and feature learning with pseudo labels are conducted alternatively to refine the pseudo labels to some extent, the training of the neural network is still substantially hindered by the inevitable label noise. The noise derives from the limited transfcrability of source-domain features, the unknown number of target-domain identities, and the imperfect results of the clustering algorithm. The refinery of noisy pseudo labels has crucial influences to the final performance, but is mostly ignored by the clustering- UDA methods.
Cooe available at httphttt/////. hhtp:///////hhbcoooocc..c. /ttcc/////ttthhb..cm//gggggeeee

---

Published as a conference paper at ICLR 2020
Naiey by by Clustoring
Robust Pamndo Labels by
A1
10
A1
Nell
Net2
1A
Figure I: Person image A and A₂ belong to the same identity while B with similar appearance is from another person. However, clustering-generate Domain Adaptation (UDA) methods contain much noise that hinders feature learning. We propose pseudo label refinery with on-line refined soft pseudo labels to effectively mitigate the influence of noisy pseudo labels and improve UDA performance on person re-ID..
pseudo labels in state-of-the-ar Unsupervised
To effectively address the problem of noisy pseudo labels in clustering based UDA methods (Song (ct al. 2018 Zhang et al. 2019b] Yang et al. 2019) (Figure D. we propose an unsupervised Mutual Mean- Teaching (MMT) framework to effectively perform pseudo label refinery by optimizing the neural networks under the joint supervisions of off-line refined hard pseudo labels and on-line refined soft pseudo labels. Specifically, our proposed MMT framework provides robust soft pseudo labels in an on-line peer-teaching manner, which is inspired by the teacher-student approaches (Tarvainen) & Valpola| 2017; Zhang al., 20186) to simultaneously train two same networks. The networks gradually capture target-domain data distributions and thus refine pseudo labels for better feature learning. To avoid training error amplification, the temporally average model of each network is proposed to produce reliable soft labels for supervising the other network in collaborative training strategy. By training peer-networks with such on-line soft pseudo labels on the target domain, the learned feature representations can be iteratively improved to provide more accurate soft pscudo labels, which, in turn, further improves the discriminativeness of learned feature representations.
The classification and triplet losses are commonly adopted together to achieve paate-of-the-at per- formances in both uully-supervised (Luo et al., 2019) and unsupervised (Zhang et al.] 2019b Yang al. 2019) person re-ID models. However, the conventional triplet loss (Hermans et al. 2017) not work with such refined soft labels. To enable using the triplet loss with soft pscudo labels in our propose a novel soft ffimmxxttriple loss that the network
MMT framework, softly refined triplet labels. The introduction of such soft softmax- superior performance of our proposed framework. Note that the collaborative training strategy on the two networks is only adopted in the training process. Only one network is kept in the inference stage without requiring any additional computational or memory cost.
benefit from loss is also the key to the
The contributions of this paper could be summarized as three-fold. (1) We propose to tackle the label noise problem in state-of-the-ard clustering- UDA methods for person re-ID, which is mostly ignored by existing methods but is shown to be crucial for achieving superior final per- formance. The proposed Mutual Mean-Tess Teaching (MMT) framework is designed to provide more reliable soft labels. (2) Conventional triplet loss can only work with hard labels. To enable train- ing with soft triplet labels for mitigating the pseudo label noise, we propose the soft softmax-tripler loss to learn more discriminative person features. (3) The MMT framework shows exceptionally strong performances on all UDA tasks of person re-ID. Compared with state-of-the-as methods, it leads to significant improvements of 14.4%. 18.2% %.. 13.4%, 16.4% mAP on Market-to-Dukke Duke-to-Market, Market-to-MSMT,, Duke-to-MSMIT re-ID tasks.
2 RELATED WORK
Unsupervised domain adaptation (UDA) for person re-ID, UDA methods have attracted much attention because their capability of saving the cost of manual annotations. There are three main categories of methods. The first category of clustering- based methods maintains state-of-the-ar per- formance to date. (Fan et al.) 2018) proposed to alternatively assign labels for unlabeled training samples and optimize the network with the generated targets. (Lin et al. 2019) proposed bottom-
2

---

Published as a conference paper at ICLR 2020
up clustering framework with repelled loss. (Yang al. 2019) introduced assign hard pseudo labels for both global and local features. However, the training of the neural network was substan- tially hindered by the noise of the hard pseudo labels generated by clustering algorithms, which was mostly ignored by existing methods. The second category of methods learns domain-invarian fea- tures from syyle-transferred et al. 2018) transformed source-domain images to match the image styles of the target domain while maintaining the original person identities. The style-transferred images and their identity labels were then used to fine-tune the model. HHL (Zhong et al., 2018) learned camera- camera-invariant features with camera style transferred images. However, the retrieval performances of these methods deeply relied on the image generation quality, and they did not explore the complex relations between different samples in the target domain. The third category of methods attempts on optimizing the neural networks with soft labels for target-domain samples by computing the similarities with reference images or features. ENC (Zhong et al., 2019) assigned soft labels by saving averaged features with an exemplar memory module. MAR Yu et al. 2019) conducted multiple soft-label learning by comparing with a set of reference persons. However, the reference images and features might not be representative enough to generate accurate labels for achieving advanced performances.
nomcie images. SPGAN (Deng et al. 2018) and PTGAN (Wei)
Generic domain adaptation methods for close-set recognition. Generic domain adaptation meth- ods learn features that can minimize the differences between data distributions of source and target
domains. Adversarial learning based methods (Zhang et al., [0188]] [Tznng et al.] 2017 Ghifary ct al. 2016: Bousmalis et al., [0166] Tzeng al., 2015) adopted a domain classifier to dispel the dis-
criminative domain information from the learned features in order to reduce the domain gap. There
also exist methods (Tzeng al., 2014: Long et al., 2015 Yan et al. 2017 Saito et al. 2018 Ghi-
fary et a 2016) that minimize the Maximum Mean Discrepancy (MMD) loss between source- and target-domain distributions. However, these methods assume that the classes on different domains are shared, which is not suitable for unsupervised domain adaptation on person re-ID.
Teacher-student models have been widely studied in semi-supervised learning methods and knowl- edge/model distillation methods. The key idea of teacher-student models is to create consistent training supervisions for labeled/unlabeled data via different models' predictions. Temporal ensem- bling (Laine & Aila 2016) maintained exponential moving average prediction for each sample as the supervisions of the unlabeled samples, while the mean-teacher model (Tarvainen & Valpola 2017) averaged model weights at different training iterations to create the supervisions for unlabeled samples. Deep mutual learning (Zhang et al. (0188) adopted a pool of student models instead of the teacher models by training them with supervisions from each other. However, existing methods with teacher-student mechanisms are mostly designed for close-set recognition problems, where both labeled and unlabeled data share the same set of class labels and could not be directly utilized on unsupervised domain adaptation tasks of person re-ID.
Generic methods for handling noisy labels can be classified into four categories. Loss correction methods (Patrini et al., 2017 Vahdat 2017] Xiao et al., 2015) tried to model the noise transition matrix, however, such matrix is hard estimate in real-world tasks, e.g. unsupervised person re- re-ID with noisy pseudo labels obtained via clustering algorithm. (Veit et al. 2017 Lee et al. 2018 Li (et al., 2017: Han et al. 2019) attempted to correct the noisy labels directly, while the clean set re- quired by such methods limits their generalization on real-world applications. Noise-robust methods designed robust loss functions against label noises, for instance, Mcan Absolute Error (MAE) loss (Ghosh et al. 2017). Generalized Cross Entropy (GCE) loss (Zhang & Sabuncu 2018) and Label Smoothing Regularization (LSR) (Szegedy et al. 2016). However, these methods did not study how to handle the triplet loss with noisy labels, which is crucial for learning discriminative feature representations on person re-ID. The last kind of methods which focused on refining the training strategies is mostly related to our method. Co-teaching (Han al. 2018) trained two collabora- tive networks and conducted noisy label detection by selecting on- line clean data for each other, Co-mining (Wang et al., 2019) further extended this method on the face recognition task with re-weighting function for Arc-Softmax loss (Deng et al., 2019). However, the above methods are not designed for the open-set person re-IID task and could not achieve state-of state-of-the-arr performances under the more challenge unsupervised settings.
3

---

Published as a conference paper at ICLR 2020
3 PROPOSED APPROACH
We propose a novel Mutual Mean-Teaching (MMT) framework for tackling the problem of noisy pseudo labels in clustering-based Unsupervised Domain Adaptation (UDA) methods. The label noise has important impacts to the domain adaptation performance but was mostly ignored by those methods. Our key idea is to conduct pseudo label refinery in the target domain by optimizing the neural networks with off-line refined hard pseudo labels and on-line refined soft pseudo labels in collaborative training manner. In addition, the conventional triplet loss cannot properly work with soft labels. A novel soft softmax-triplet loss is therefore introduced to better utilize the softly refined pseudo labels. Both the soft classification loss and the soft softmax-tripe -trilet loss work jointly to achieve optimal domain adaptation performances.
)[ Formally, we denote the source domain data as D. {xx] yt 1 the i-th training sample and its associated person identity label, No is the number of images, and M, denotes the number of person identities (classes) in the source domain. The Nr target-domain N;
where x; and yf denote
images are denoted as Dr = { ki=1) }, which are not associated with any ground-truth identity label.
3.1 CLUSTERINGGBBSSED UDA METHODS REVISIT
State-of- saate-of-the-ar art UDA methods (Fan et al., 2018 Lin et al., 2019: Zhang al.) 2019b Yang et al.
2019) follow a similar general pipeline. They generally pre- train deep neural network F F- on the source domain, where 0 denotes current network parameters, and the network is then transferred to learn from the images in the target domain. The source-domain images' and target-domain images' features encoded by the network are denoted {F(x [0) | and (F(xr) } respectively. As illustrated in Figure 2
(a), two operations are alternated to gradually fine-tune the pre-trained network on the target domain. (I) The target-domain samples are grouped into pre-defined M, classes by clustering the features {F( | output by the current network, Let of denotes the pseudo label generated for image æ/. (2) The network parameters 0 and a learnable target-domain classifier C f + f1, MM} are then optimized with respect to an identity classification (cross- entropy) loss Ci (0) and a triplet loss (Hermans et al. 2017) Cin (0) in the form of,
N2
(0) No 2m1
Σ
Lee (C'(F(a))))) ut),
(l, = N₂
max (0, [| - Flzi,p 0)1 + m m- -IIP(x))0) - F(a 00)l)
(2)
where ||- || denotes the I-2-norm distance, subscripts i.p and i, indicate the hardest positive and hardest negative feature index in each mini-batch for the sample æ². and m = 0.5 denotes the triplet distance margin. Such two operations, pseudo label generation by clustering and feature learning with pseudo labels, are alternated until the training converges. However, the pseudo labels generated in step (1) inevitably contain errors due the imperfection of features as well as the errors of the clustering algorithms, which hinder the feature learning in step (2). To mitigate the pseudo label noise, we propose the Mutual Teachine (MMT) framework together with a novel soft softmax-triple loss to conduct the pseudo label refinery.
3.2 MUTUAL MEAN-TEACHING TEACHING (MMT) FRAMEWORK
3.2.1
SUPERVISED PRE-TRE TRAINING FOR SOURCE DOMAIN
UDA task on person re-ID aims transferring the knowledge from a pre-trained model on the source domain to the target domain. A deep neural network is first pre-trained on the source domain. Given the training data D.. the network is trained to model a feature transformation function F(- that transforms each input sample xf into feature representation F(x; (0). Given the encoded features, the identification classifier C* outputs an M -ddiinnnio probability vector to predict the identities in the source-domain training set. The neural network is trained with classification loss L (0) and a triplet loss Lip; (0) to separate features belonging to different identities. The overall loss is therefore calculated as
(² (0) + X (O).
(3)
where (0) and (0) are defined similarly to equation and equation 2 but with ground-truth identity labels {vt }. and X is the parameter weighting the two losses.
4

---

Published as a conference paper at ICLR 2020
(a) General pipline for clustering based UDA methods
(b) The proposed Mutual Mean- (MITT) framework
P(-91)
F(-16g))
Predictions
Predictions
Nobay Hard
Net
Pscudd Labels
Net
Choster
AA
Paerada Lobseh
Nokoy Flanl
Soft Clavefiiattio Lavs
Temporal Avcrase
Troporal
Anerige
(c) Inference stage with only one networkk for MMT
FL. R(M)
Mean
Prodictions
Triplet
Prodictions
Mean
Net
Net
Figure 2: (a) The pipeline for existing clustering- based UDA methods on person re-ID with noisy hard pseudo labels. (b) Overall framework of the proposed Mutual Mcan- Teaching (MMT) with two collaborative networks jointly optimized under the supervisions of off-line refined hard pseudo labels and on-line refined soft pseudo labels. A soft identity classification loss and novel soft coftmax-triple loss are adopted. (c) One of the average models with better validated performance is adopted for inference as average models perform better than models with current parameters.
3.2.2 PSEUDO LABEL REFINERY WITH ON-LINE REFINED SOFT PSEUDO LABELS
Our proposed MMT framework is based on the clustering- -ased UDA methods with off-line refined hard pseudo labels as introduced in Section 3.1 where the pseudo label generation and refinement are conducted alternatively. However, the pseudo labels generated in this way are hard (i.e., they are always of 100% confidences) but noisy. In order to mitigate the pseudo label noise, apart from the off-line refined hard pscudo labels, our framework further incorporates on-line refined soft pseudo labels (i.e., pseudo labels with 100% confidences) into the training process.
Our MMT framework generates soft pseudo labels by collaboratively training two same networks with different initializations. The overall framework is illustrated in Figure 2 (b). The pseudo classes are still generated the same as those by existing clustering- based UDA methods, where each cluster represents one class. In addition the hard and noisy pseudo labels, our two collaborative networks also generate on-line soft pseudo labels by network predictions for training each other. The intuition is that, after the networks are trained even with hard pseudo labels. they can roughly capture the training data distribution and their class predictions can therefore serve as soft class labels for training. However, such soft labels are generally not perfect because of the training errors and noisy hard pseudo labels in the first place. To avoid two networks collaboratively bias each other, the past temporally average model of each network instead of the current model is used to generate the soft pseudo labels for the other network. Both off-line hard pseudo labels and on-line soft pscudo labels are utilized jointly to train the two collaborative networks. After training, only one the past average models with better validated performance is adopted for inference (see Figure (c)).
We denote the two collaborative networks feature transformation functions F(- and F(-102). and denote their corresponding pseudo label classifiers as Cf and C22, respectively. To simulta- neously train the coupled networks, we feed the same image batch to the two networks but with separately random erasing, cropping and flipping. Fach target-domain image can be denoted by of and xt for the two networks, and their pseudo label confidences can be predicted as (F(({))))
and C,2( (=x")())) One naive way to train the collaborative networks is to directly utilize the above pseudo label confidence vectors as the soft pseudo labels for training the other network. However, in such a way, the two networks' predictions might converge to equal each other and the two networks lose their output independences. The classification errors as well as pseudo label errors might be amplified during training. In order to avoid error amplification, we propose to use the temporally average model of each network to generate reliable soft pseudo labels for supervising the other net- work. Specifically, the parameters of the temporally average models of the two networks at current iteration T' are denoted as E(T)())
[0,) and E(T) (2)) respectively, which can be calculated
E(T) 0 aF (7-1) (T [0,) + (1-a)0).
TT 0z) (T (0) + (1 a2)2..
(4)
where ((,)))(0,), ((T-1)((0))) indicate the temporal average parameters of the two networks in the previous iteration (T-1) the initial temporal average parameters are F.(0) [[)) 0,, F(0 (0z)
Og.
5

---

Published as a conference paper at ICLR 2020
and is the ensembling momentum to be within the range [0, 1). The robust soft pseudo label supervisions then generated by the two temporal average models C((F(x)(E(())))11) and (F(a")]B() respectively. The soft classification loss for optimizing 01 and O2 with the soft pseudo labels generated from the other network can therefore be formulated as
No
(0:
1
Σ
log (FFTe
No
N
(2ia(02(01)
Σ No (Ci(F()))))))) log C1((((')))))
(S)
The two networks' pseudo-label predictions are better dis-related by using other network's past average model to generate supervisions and can therefore better avoid error amplification.
Generalizing classification cross- coos--entrppy loss to work with soft pseudo labels has been well studied (Hinton al. (013), (Müller al., 2019). However, optimizing triplet loss with soft pseudo labels poses great challenge as no previous method has investigated soft labels for triplet loss. For lackling the difficulty, we propose to use softmax-triplet loss, whose hard version is formulated as
N₂
(0)) = N
Loce Ti((1):
where
exp(1]F((a:
01) F( (0))l)
T.(0:)=
(1) + exp(1 (a) 01) (0))il
Here :) denotes the binary cross-entropy loss, 0(x)((11) is the encoded feature for target- domain sample æę by network 1, the subscripts i, and i.n denote sample zf's hardest positive and negative samples in the mini-batch, [I
F( is the 1,²-norm distance between sample æ! and its positive sample æi.,, to measure their similarity, and "1" denotes the ground-truth that the positive sample æt,, should be closer the sample xf than its negative sample æ Given the two collaborative networks, can utilize the one network's 's past temporal average model to generate soft triplet labels for the other network with the proposed soft softmax- -ripltt loss,
(0)r((1 =
1
Cace Ti((1), Ti ( (Bal))
No
N.
C. (0210:)
1
Σ
Locs
Lace (T((2)); Ti (0.1))
(8)
N.
where (T)) and (((E(T) are the soft triplet labels generated by the two networks' past temporally average models. Such soft triplet labels are fixed as training supervisions. By adopting the soft softmax-triplet loss, our MMT framework overcomes the limitation of hard supervisions by the conventional triple loss (equation 2). It can be successfully trained with soft triplet labels, which shown to be important for improving the domain adaptation performance in our experiments. Note that such softmax-triphe loss was also studied in (Zhang et al.) 2019a). However, it has never been used generate soft labels and was not designed to work with soft pseudo labels before.
3.2.3 OVERALL LOSS AND ALGORITHM
Our proposed MMT framework is trained with both off-line refined hard pseudo labels and on-line refined soft pseudo labels. The overall loss function L(,,, 02) simultancously optimizes the coupled networks, which combines equation [ equation equation equation and is formulated as,
C(O,, 02) (1 Caa (0:) (l2) + (i (0. (0,10)))
(9) where Mt, X are the weighting parameters. The detailed optimization procedures are summarized in Algorithm The hard pseudo labels are off-line refined after training with existing hard pseudo labels for one epoch. During the training process, the two networks are trained by combining the off- line refined hard pseudo labels and on-line refined soft labels predicted by their peers with proposed soft losses. The noise and randomness caused by hard clustering, which lead to unstable training and limited final performance, be alleviated by the proposed MMT framework.
+ (1 (C. + (i.,
(0. L. (02)0)).

---

Published as a conference paper at ICLR 2020
Require: Target-dormain dormain data Ds;
Require: Ensembling momentum for equation weighting factors Mia Xi for equation Require: Initialize pro-trained weights 92 and 02 by optimizing with equation Blon Dx. for num.epochs]]
Generate hard pseudo lahels y/ for each sample æn in D, by clustering algorithmss.
mini-hatch
Dr, iteration
I: Generate soft pscudo labels from the collaborative networks by predicting The ( (e
(F(nee (mle a (0,))) C3 (F(ar" ((rrie
(0x)))
2: Joint update parameters 02 & 0g by the gradient descent of the objective functiion equation
3: Update temporally average model weights g 32*+1((0.) [@:] & [02+ following equation
end
end for
Algorithm 1: Unsupervised Mutual Mean- Beaa--Tecching (MMT) Training Strategy
4 EXPERIMENTS
4.1 DATASETS
We evaluate our proposed MMT on three widely-used person re-ID datasets, i.e. Market- 1501 (Zheng et al. 2015), DukcMTMC-DeDD (Ristani et al. 2016), and MSMT17 (Wei al. (0188).
The Market-150h (Zeng et al. 2015) dataset consists of 32,668 annotated images of 1,501 identi-
ties shot from 6 cameras in total, for which 12,936 images of 751 identities are used for training and 19,732 images of 750 identities are in the test set. DukeMTMC-reID (Ristani et al. 2016) contains 16,522 person images of 702 identities for training, and the remaining images out of another 702
identities for testing, where all images are collected from cameras, MSMT17 (Wei et al. 2018) is the most challenging and large-scale dataset consisting of 126,441 bounding boxes of 40101 identi- ties taken by 15 cameras, for which 32,621 images of 1,041 identities are spitted for training. For evaluating the domain adaptation performance of different methods, four domain adaptation tasks are set up, i.e., Duke-to-Market, Market-to-Duke, Duke-to-MSMIT and Market-to-MSMTT where only identity labels on the source domain are provided. Mean average precision (mAP) and CMC top-1, top-5, top-10 10 accuracies are adopted to evaluate the methods' performances.
4.2
IMPLEMENTATION DETAILS
4.2.1
TRAINING DATA ORGANIZATION
For both source-domain pre-training and target-domain fine-tuning, each training mini-batch con- tains 64 person images of 16 actual or pseudo identities (4 for each identity). Note that the generated hard pseudo labels for the target-domain fine-tuning are updated after each epoch, so the mini-batch of target-domain images needs to be re-organized with updated hard pseudo labels after each epoch. All images are resized to 256 x 128 before being fed into the networks.
4.2.2 OPTIMIZATION DETAILS
All the hyper-parameters of the proposed MMT framework are chosen based a validation set of the Duke-to-Marken task with M, 500 pseudo identities and IBN-ResNet-50 backbone. The same hyper-parameters are then directly applied the other three domain adaptation tasks. We propose a two-stage training scheme, where ADAM optimizer is adopted to optimize the networks with weight decay of 0.0005. Randomly erasing (Zhong et al. 2017b) is only adopted in target-domain fine-tuning.
Stage 1: Source-domain pre-training. We adopt ResNet-50 (He et al. 2016) IBN-ResNet-50 (Pan et al. 2018) as the backbone networks, where IBN- ResNet50 achieves better performances by integrating both IN and BN modules. Two same networks are initialized with ImageNet (Deng al.) 2009) pre-trained weights. Given the mini-batch of images, network parameters 0,, 02 are updated independently by optimizing equation 3 with \= 1. The initial learning rate is set to 0.00035 and is decreased to 1/10 of its previous value on the 40th and 70th epoch in the total 80 epochs.
Stage 2: End-to-end training with MMT. Based on pre-trained weights O, and 02, the two networks are collaboratively updated by optimizing equation 9 with the loss weights X 0.5. X = 0.8. The temporal ensemble momentum a in equation is set to 0.999. The learning rate is fixed to 0.00035 for overall 40 training epochs. We utilize k-means clustering algorithm and the number M, of pseudo classes is set as 500, 700, 900 for Market- 150k and MeCr-IDC and 500, 1000, 1500, 2000 for MSMT17. Note that actual identity numbers in the darget-domain training

---

Published as a conference paper at ICLR 2020
Markot-to-Duke
Duke-to-Market
Methods
mAP
tup
top-5
top-TU
mAP
lup
top
top TO
2015) (TOMM'18)
30.0
48.5
60.7
TJAAD
Wany arar
2018 (CVPR'18)
23.0
44.3
59.6
65.0
26.5
58.2
74.8
81.1
SPGAN
Denz cl 2 (8)))'18
22.3
41.1
56.6
63.0
22.8
51.5
70.1
76.8
I
(hiny
2018 CUTSSTEEE (BCCV"18)
27.2
46.9
61.0
66.7
31.4
62.2
78.8
84.0
CFSM
CFSMrChang.cco of 2019
(AAAT19)
27.3
49.8
28.5
61.2
BUC
Lin (019)) AAAA99
27.5
47.4
68.4
38.3
79.6
84.5
ARN Hr 2015) (CVPR 18-W1)
33.4
60.2
73.9
79.5
39.4
70.3
80.4
86.3
UDAP (Song 2018
(Arxiv'18)
53.7
89.5
ENC Zliong ara (2019))
(CVP) 19)
(CVPR'
40.4
63.3
75.8
80.4
43.0
75.1
87.6
91.6
UCDA-CCE (Q 22119 (ICCV'19)
31.0
47.7
30.9
60.4
PDA-Net
[ al (0 (ICC)
45.1
63.2
82.5
47.6
75.2
86.3
90.2
PCB-PAST (Zhang ef al 2019b (ICCV'19)
54.3
72.4
54.6
78.4
Yang
2019)
73.0
83.2
58.3
Co-tenching (Han ct 2018-500 (ResNer-50)
55.7
71.9
83.5
88.1
65.1
82.5
91.8
93.4
Co-teaching er
2018 (IB--RosNN
61.7
77.6
90.7
71.7
95.0
96.5
Pre-trained (ResNct-5U)
46.0
31.8
76.4
Proposed MMT-500 (R))Net-50)
63.1
76.8
88.0
92.2
71.2
87.7
94,99
96.9
Proposed MMF-770 (ResNet-50)
78.0
94.6
Proposed MMT-900 (ResNet-50)
63.1
77.4
88.1
92.5
66.2
86.8
94,9
96.6
Pre treitraied (IBN: ReeNee- 50)
35.4
54.0
67.7
72.9
35.6
65.3
79.7
84.3
Proposed MMT-500 (IBN-ResNet-50)
65.7
79.3
89.1
92.4
76.5
96.4
97.9
Proposed MMT 700 (IBN ResNet
68.7
81.8
91.2
93.4
74.5
91.1
96.5
98.2
Proposed MMT-900 (IBN-ResNet-50)
80.8
93.0
72.7
96.3
Methods
Market-to- Marrket-to-MSSMI
Duke-to IO-M
mAP
Top-1
top-5
mAP
top
top
PTGAN (Wei et 2018) (CVPR'1S)
2.9
10.2
24.4
3.3
11.8
27.4
ENC [Zhong
2019 (CVPR'19)
8.5
25.3
36.3
42.1
10.2
30.2
41.5
46.8
SSG Yang clal.
20191 (ICCV'19)
13.2
31.6
49.6
13.3
32.2
51.2
Pre-trained (KesNet-S1)
9.4
38.1
Proposed MMT-500 (ResNct-50)
16.6
37,5
50.6
56.5
17.9
41.3
54,2
59.7
Proposed MMT 1000 (ResNel-50)
21.6
46.1
59.8
66.1
23.5
50.0
63.6
69.2
Proposed MMT--100 (ResNet-50)
22.9
49.2
68.8
23.3
63.9
69.8
Proposed MMT--200 (ResNet-50)
20.8
45.7
39.6
63.6
22.4
49.0
62.5
67.8
Pre-trained (IBN-ResNet-50)
25.3
44.7
Proposed MMT-500 (IBN-ResNet-50)
19.6
43.3
56.1
61.6
23.3
50.0
62.8
68.4
Proposed MMT1-100 (IBN- (BN-ResNet-50)
26.3
52.5
66.3
71.7
29.7
58.8
71.0
76.1
Proposed MMTT-1500 (IBN-ResNct-50)
54.4
72.9
29.3
71.6
76.8
Proposed MMT-2000 (BBN-ResNet-50)
25.1
52.7
65.9
71.3
28.1
56.8
70.8
76.0
Table 1:
Experimental results of the proposed MMT and state-of-the-art methods on Market- 1501 (Zheng et al.) 2015), DukeMTMC-relll (Ristani et al.) (016), and MSMT17 (Wei et al.) 2018) datasets, where MMT-M, represents the result with M, pseudo classes. Note that none of M, values equals the actual number of identities but our method still outperforms all state-of-the-arts.
sets are different from M,. We test different M, values that either smaller greater than actual numbers.
4.3 COMPARISON WITH STATE-OF-THE-ARE
We compare proposed MMT framework with state-of-the-art methods the four domain adap- tation tasks, Market-to-Duke Duke-to-Market Market-toMMSMT and Duke-to-MSMT.. The results are shown in Table [ Our MMT framework significantly outperforms all existing approaches with both ResNet-50 and IBN-ResNet-50 backbones, which verifies the effectiveness of our method.
Moreover, we almost approach fully-supervised learning performances (Sun al. 2018 Ge al. 2018) without any manual annotations on the target domain. No post-processing technique, e.g. re-ranking (Zhong et al. 2017a) or multi-query fusion (Zheng et al. 2015), is adopted.
Specifically, by adopting the ResNet-50 (He et al. 2016) backbone, we surpass the state-of-the- art clustering-based SSG (Yang et al. 2019) by considerable margins of 11.7% and 12.9% mAP on Market-to-Duke and Duke-to-Market tasks with simpler network architectures and lower output feature dimensions. Furthermore, evident 9.7% and 10.2% mAP gains are achieved on Market-to- MSMT and Duke-to-MSMI tasks. Recall that M, is the number of clusters number of hard pseudo labels manually specified. More importantly, we achieve state-of-the- all tested target datasets with different M,, which are either fewer more than the actual number of identities in the training set of the target domain. Such results prove the necessity and effectiveness of our proposed pseudo label refinery for hard pseudo labels with inevitable noises.
performances on
8

---

Published as a conference paper at ICLR 2020
JeN-KesNet-90
Net-er-50
Duke-to Morkkke
mAP
top
top-5
top 10
mAP
tap
tep
tap 10
Pra-trained (omly Lia & L.rd)
35.6
65.3
79.7
84.3
31.8
61.9
76.4
82.2
Baseline (only Ci & (irs)
62.7
84.4
92.7
95.5
53.5
76.0
88,1
91.9
Baseline-MMT-SS0 (only Reid & fistrs)
34.5
59.7
73.0
78.0
22.4
46.5
61.5
67.4
Baeline-MMT-SU0 (wlo (4a)
38.0
79.4
24.9
64.0
69.8
Baseline-MMMT-500 (w/o
Buscline-MMT-S00 (wro C (nn))
76.2
90.8
96.6
97.9
72.0
87.8
95.5
96.9
69.6
87.4
95.22
96.7
62.6
84.0
93.4
95.4
Bacllin-MMTT (wla Carer)
71.7
88.5
95.1
96.6
65.9
84.0
93.1
95.5
Basclinc-MMMI-300 82))
72.8
97.1
67.5
96.1
Baeeline-MMT-S00 (w/o E
72.1
88.7
95.4
97.3
62.3
80.5
91.3
94.0
Biscline-MMMJ330
76.5
90.9
96.4
97.9
71.2
87.7
94.9
96,9
TAN ReN-Re
ResNet-50
Market-to-Duke
mAP
lop-1
1pp-5
lop-10
mAP
lop-1
kop
lop-10 10
Pre-trained (only Lia & Cird
35.4
54.0
67.7
72.9
29.6
46.0
61.5
67.2
Baseline (only Cla & Cirs)
55.0
72.3
84.4
88.1
48.2
66.4
79.8
84.0
Baseline-MMT.5 (only Cisi fistrs)
24.5
36.4
42.5
Baseline-MMT (wlo C5
27.5
42.0
53,9
60.3
15.3
25,8
37.7
43.7
Baeline-MMT- (w/o C (.ve))
65.6
79.4
89.8
92.3
63.0
77.3
88.3
91.6
Buseline-MMT-500 (wlo C
60.3
75.7
86.6
89.9
58.1
74.9
85.2
89.5
Baelinn--MMT--000 C
61.7
77.1
89.6
59.5
73.9
85.5
88.8
Bascline-MMM]-500 (wHl 02)
62.1
77.6
86.8
89.7
58.2
74.1
86,0
89.3
Baselne-MMMT-SU (w/o E(0')
61.1
76.3
86.6
89.8
55.7
70.0
83.6
87.2
Basclinc-NMM13000
65.7
79.3
92.4
63.1
76.8
88.0
92.2
Table Ablation studies of our proposed MMT on Maraket and Market-to- Duke tasks with M, of 500. Note that the actual numbers of identities are not equal to 500 for both datasets but our MMT method still shows significant improvements.
To compare with relevant methods for tackling general noisy label problems, we implement Co- teaching (Han et al. 2018) on unsupervised person re-ID task with 500 pseudo identities on the target domain, where the noisy labels are generated by the same clustering algorithm framework. The hard classification (cross-entropy) loss is adopted on selected clean batches. All the hyper-parameters are
MMT
the same for fair comparison, and the experimental results are denoted as "Co-teaching (Han et al. 2018)- 2018)-5001 5-000 with both ResNet-50 and IBN 1BN-ResNet-50 backbones in Table I Comparing "Co-teaching (Han al., 2018) (ResNet-50)" with "Proposed MMT-500 (ResNet-50)". we observe significant 7.4% and 6% % mAP drops on Market-to-Dukk and Duke- to-Market tasks respectively, since Co-teaching (Han et al. 2018) designed for general close-set recognition problems with manually generated label noise, which could not tackle the real-world challenges in unsupervised person re-ID, More importantly, does not explore how to mitigate the label noise for the triplet loss as our method does.
4.4 ABLATION STUDIES
In this section, we evaluate each component in our proposed framework by conducting ablation studies on Duke-to-Market and Market-to-Duke tasks with both ResNet-50 (He et al. 2016) and IBN-ResNet-50 (Pan et al. 2018) backbones. Results are shown in Table 2
Effectiveness of the soft pseudo label refinery. To investigate the necessity of handling noisy pseudo labels in clustering-based UDA methods, we create baseline models that utilize only off-line refined hard pseudo labels, i.e., optimizing equation 9 with Mia = X for the two-step training strategy in Section 3.1 The baseline model performances are present in Table 2 "Baseline (only Cta & Yr. Considerable drops of 17.7% and 14.9% mAp are observed on ResNet-50 for Duke- to-Market and Market-to-Dae Duke tasks. Similarly, 13.8% and 10.7% mAP decreases are shown on the backbone. Stable increases achieved by the proposed on-line refined soft pseudo labels on different datasets and backbones demonstrate the necessity of soft pseudo label refinery and the effectiveness of our proposed MMT framework.
Effectiveness of the soft softmax-triplet loss. We also verify the effectiveness of soft softmax- triplet loss with softly refined triplet labels in our proposed MMT framework. Experiments of removing the soft softmax-triplet loss, i.e., X = 0 in equation 9 but keeping the hard softmax- triplet loss (equation 6 are conducted, which are denoted as "Baseline+MMMT-50 (w/o L Lr All experiments without the supervision of soft triplet loss show distinct drops on Duke-to-Marke and Market-to- Duke tasks, which indicate that the hard pseudo label with hard triplet loss hinders the feature learning capability because it ignores pseudo label noise by the clustering algorithms.

---

Published as a conference paper at ICLR 2020
Specifically, the mAP drops are 5.3% on ResNet-50 and 4.8% IBN-ResNet-Se when evaluating on the target dataset Market-1500 As for the Market-to-Dukce task, similar mAP drops of 3.6% and 4.0% on the two network structures can be observed. An evident improvement of up to 5.3% mAP demonstrates the usefulness of our proposed soft softmax-triplet loss.
Effectiveness of Mutual Mean- Teaching.. We propose to generate on-line refined soft pseudo labels for one network with the predictions of the past average model of the other network in our MMT framework, i.e., the soft labels for network are output from the average model of network and vice versa. We observe that the soft labels generated in such manner are more reliable due to the better decoupling between the past temporally average models of the two networks. Such a framework could effectively avoid bias amplification even when the networks have much erroneous outputs in the early training epochs. There are two possible simplification our MMT framework with less de-coupled structures. The first one is to keep only one network in our framework and use its past temporal average model to generate soft pseudo labels for training itself. Such experiments are denoted as MMMT5500 (w/o 02)". The second simplification is to naively use one network's 's current-iteration predictions as the soft pseudo labels for training the other network and vice versa, i.e., a 0 for equation 4 This set of experiments are denoted "Baseline+MMMT-S00 (w/o E(o)". Significant mAP drops compared to our proposed MMT could be observed in the two sets of experiments, especially when using the ResNet-50 backbone, e.g. the mAP drops by 8.9% on Duke-to-Market task when removing past average models. This validates the necessity of employing the proposed mutual mean-teaching scheme for providing more robust soft pseudo labels. In despite of the large margin of performance declines when removing either the peer network or the past average model, our proposed MMT outperforms the baseline model significantly, which further demonstrates the importance of adopting the proposed on-line refined soft pseudo labels.
Necessity of hard pseudo labels in proposed MMT. Despite the robust soft pseudo labels bring significant improvements, the noisy hard pseudo labels are still essential to our proposed frame- work, since the hard classification loss Ltd is the foundation for capturing the targer-domain data distributions. To investigate the contribution of Lid in the final training objective function as equa- tion 9 we conduct two experiments. (1) MBase50n++MMM-5000 (only Li & C'rre)" " by removing both hard classification loss and hard triplet loss with X Xyys 1; (2) 22"BaselineMMMTTTS (w/o 2',)" by removing only hard classification loss with Xra = 1. As illustrated in Table 2 the above experiments both result in much lower performances than the model pre-trained on the source domain ("Pre-trained (only Lig & )"r:)"), which effectively validate the necessity of Ctg- The initial network usually outputs uniform probabilities for each identity, which act as soft labels for soft classification loss, since it could not correctly distinguish between different identities on the target domain. Directly training with such smooth and noisy soft pseudo labels. the networks in our framework would soon collapse due to the large bias. One-hot hard labels for classification loss are critical for learning discriminative representations on the target domain. In contrast, the hard triplet loss Ltr; is not absolutely necessary in our framework, as experiments without Live- denoted as "Baseline+MMT-500 (w/o C(,,)" with Afrs = 1.0, show similar performances as our final results with Mfr; 0.8. is much easier to learn to predict robust soft labels for the soft softmax-triplet loss in equation even at early training epochs, which has only two classes, i.e., positive and negative.
CONCLUSION
In this work, we propose an unsupervised Mutual Mean-Teaching (MMT) framework to tackle the problem of noisy pseudo labels in clustering- unsupervised domain adaptation methods for person re-ID. The key is to conduct pseudo label refinery to better model inter-sample relations in the target domain by optimizing with the off-line refined hard pseudo labels and on-line refined soft pscudo labels in a collaborative training manner. Moreover, a novel soft softmax-triple loss is proposed to support learning with softly refined triplet labels for optimal performances. Our method significantly outperforms all existing person re-ID methods on domain adaptation task with up to 18.2% improvements.
ACKNOWLEDGMENNT
This work is supported by the General Research Fund sponsored by the Research Grants Council of Hong Kong (Nos. CUHK JUHK14408417. 12HK44239 CUHK 142073199, the Hong Kong Innova- tion and Technology Support Program (No. ITS/312/18FX).
10

---

Published as a conference paper at ICLR 2020
REFERENCES
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan. Domain separation networks, In NIPS, 2016.
Xiaobin Chang, Yongxin Yang, Tao Xiang, and Timothy M Hospedales. Disjoint label space transfer learning with common factorised space. AAAI, 2019.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. 2009.
Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Areface: Additive angular margin loss for deep face recognition. In CVPR, pp. 4690-4699, 2019.
Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, and Jianbin Jiao. Image- image domain adaptation with preserved self-similarity and domain-dissimllarity for person identification. In CVPR, 2018.
Hehe Fan, Liang Zheng, Chenggang Yan, and Yi Yang. Unsupervised person re-identification: Clustering and fine-tuning. 2018.
Yixiao Ge, Zhuowan Li, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, al. Fd-gan: Pose- guided feature distilling gan for robust person re-identification. In NeurlPS, 2018.
Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, and Wen Li. Deep reconstracion-claasiffccioo networks for unsupervised domain adaptation. In ECCV, 2016.
Aritra Ghosh, Himanshu Kumar, and PS Sastry. Robust loss functions under label noise for deep neural networks. In AAAI, 2017.
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In NIPS, pp. 8527-8537, 2018.
Jiangfan Han, Ping Luo, and Xiaogang Wang. Deep self-learning from noisy labels. In ICCV, 2019.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, 2016.
Alexander Hermans, Lucas Beyer, and Bastian Leibe. In defense of the triplet loss for person identification. arXiv preprint arXiv: 1703007737 2017.
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge neural network. arXiv preprint 103.051503.02531, 2015.
Samuli Laine and Timo Aila. Temporal ensembling for sumi-supereised learning. arXiv preprint 1r0:02161..0222,, 2016.
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang. Cleannet: Transfer learning for scalable image classifier training with label noise. In CVPR, pp. 5447-5456, 2018.
Yu-Jhe Li, Fu-En Yang, Yen-Cheng Liu, Yu-Ying Yeh, Xiaofei Du, and Yu-Chiang Frank Wang- Adaptation and re-identification network: An unsupervised deep transfer learning approach to person re-identification. In CVPRW, 2018.
Yu-Jhe Li, Ci-Siang Lin, Yan-Bo Lin, and Yu-Chiang Frank Wang. Cross-dataset person identification via unsupervised pose disentanglement and adaptation. ICCV, 2019.
Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, and Li-Jia Li. Learning from noisy labels with distillation. In ICCV, pp. 1910-1918, 2017.
Yutian Lin, Xuanyi Dong, Liang Zheng, Yan Yan, and Yi Yang. A bottom-up clustering approach to unsupervised person re-identification. In AAAI, 2019.
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. arXiv preprint
2015.

---

Published as a conference paper at ICLR 2020
Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, and Wei Jiang. Bag of tricks and strong baseline for deep person re-identification. In CVPRW, 2019.
Rafael Müller, Simon Kornblith, and Geoffrey Hinton. When does label smoothing help? arXiv preprint arXiv: 02629, 2019.
Xingang Pan, Ping Luo. Jianping Shi, and Xiaoou Tang. Two at once: generalization capacities via ibn-net. In ECCV, 2018.
Enhancing learning and
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust label noise: loss correction approach. In CVPR, pp. 1944-1952, 2017.
Lei Qi, Lei Wang, Jing Huo, Luping Zhou, Yinghuan Shi, and Yang Gao. A novel unsupervised camera- aware domain adaptation framework for person re-identification. ICCV, 2019.
Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi. Performance mea- sures and data set for multi-targer multi-cames tracking. In ECCVW, 2016.
Kuniaki Saito, Kohei Watanabe, Yoshitaka Ushiku, and Tatsuya Harada. Maximum classifier dis- crepancy for unsupervised domain adaptation. In CVPR, 2018.
Liangehen Song, Cheng Wang, Lefei Zhang, Bo Du Qian Zhang, Chang Huang, and Xinggang Wang. Unsupervised domain adaptive re-identification: Theory and practice. arXiv preprint OrXiv:/807.11334 2018.
Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). ECCV, 2018.
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, pp. 1228822266 2016.
Antti Tarvainen and Harri Valpola. Mean teachers better role models: Weight-averaged consis- tency targets improve semi-supervised deep learning results. In NIPS, 2017.
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv: 2014.
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko. Simultaneous deep transfer across domains and tasks. In CVPR, 2015.
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. CVPR, 2017.
Arash Vahdat. Toward robustness against label noise in training deep discriminative neural networks. In NIPS, pp. 5596-5605, 2017.
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie. Learning from noisy large-scale datasets with minimal supervision. In CVPR, pp. 839-847, 2017.
Jingya Wang, Xiatian Zhu, Shaogang Gong, and Wei Li. Transferable joint attribute-denniiy deep learning for unsupervised person re-identification. In CVPR, 2018.
Xiaobo Wang, Shuo Wang, Jun Wang, Hailin Shi, and Tao Mei. Co-mining: Deep face recognition with noisy labels. In ICCV, pp. 9358-9367, 2019.
Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. Person transfer gan to bridge domain gap for person re-identification. In CVPR, 2018.
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. Learning from massive noisy labeled data for image classification. In CVPR, pp. 2691-2699, 2015.
Hongliang Yan, Yukang Ding, Peihua Li, Qilong Wang, Yong Xu, and Wangmeng Zuo. Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation. In CVPR, 2017.
12

---

Published as a conference paper at ICLR 2020
Fu Yang. Wei Yunchao, Wang Guanshuo, Zhou Yuqian, Shi Honghui, and Huang Thomas. Self- similarity grouping: A simple unsupervised cross domain adaptation approach for person re- identification. ICCV, 2019.
Hong-Xing Yu, Wei-Shi Zheng, Ancong Wu, Xiaowei Guo, Shaogang Gong, and Jian Hun-Huang Lai. Unsupervised person re- re-identification by soft multilabel learning. In CVPR. 2019.
Ji Zhang, Yannis Kalantidis, Marcus Rohrbach, Manohar Paluri, Ahmed Elgammal, and Mohamed Elhoseiny. Large-scale visual relationship understanding. In AAAI, 2019a.
Weichen Zhang, Wanli Ouyang, Wen Li, and Dong Xu. Collaborative and adversarial network for unsupervised domain adaptation. CVPR, 2018a.
Xinyu Zhang, Jiewei Cao, Chunhua Shen, and Mingyu You. Self-training with progressive augmen- tation for unsupervised cross-domain person re-identification. ICCV, 2019b.
Ying Zhang, Tao Xiang. Timothy M Hospedales, and Huchuan Lu. Deep mutual learning. CVPR, 2b.
Zhilu Zhang and Mert Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In NIPS, pp. 8778 8788, 2018.
Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In ICCV, 2015.
Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li. Re-ranking person re-identification with k-reciprocal encoding. In CVPR, 2017a.
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yang. Random erasing data augmen- tation. arXiv preprint arXiv: 170.00660, 2017b.
Zhun Zhong, Liang Zheng, Shaozi Li, and Yi Yang. Generalizing person retrieval model hetero- and homogeneously. ECCV, 2018
Zhun Zhong, Liang Zheng. Zhiming Luo, Shaozi Li, and Yi Yang. Invariance matters: Exemplar memory for domain adaptive person re-identification. In CVPR, 2019.
A APPENDIX
A.I FUNCTIONS OF TEMPORAL AVERAGE MODELS IN MMT
0.008
Proposed MMI-520
WMT--00
0.000
Wedittinn
0.2055
0.0M
0.0001
Figure 3: The predictions of temporal average models (denoted as "Proposed MMT-500") serve as more complementary and robust soft pseudo labels than those of ordinary networks (denoted as "Proposed MMT-500 (w/o E(O))).
Two temporal average models are introduced our proposed MMT framework to provide more complementary soft labels and avoid training emor amplification. Such average models more
13

---

Published as a conference paper at ICLR 2020
de-coupled by ensembling the past parameters and provide more independent predictions, which is igmored by previous methods with peer-teaching strategy (Han et al. 2018: Wang et al. 2019: Zhang et al. 2018b). Despite we have verified the effectiveness of such design in Table by removing the temporal average model, denoted as "Baseline+MMTT-S (w/o F(O)". we would like to visualize the training process by plotting the KI. divergence between peer networks' predictions for further comparison. As illustrated in Figure the predictions by two temporal average models ("Proposed MMT-500") always keep a larger distance than predictions by two ordinary networks ("Proposed MMT-500 (w/o E' ") which indicates that the temporal average models could prevent the two networks in our MMT from converging to each other soon under the collaborative training strategy.
A.2 PARAMETER ANALYSIS
Impact Als to Market
mppac Ab, Market Duke
90
10.1
2g.
00.0
86.
027
70.0
060
77
72.5
Reskven sa (rear)
Ressec-s0 30 (m4-)
Produan-S0 50 (rup-
70.0
Rex Dop-
E0--Resiee--5 (mit))
(DN-Resstet--0 50 (mAP)
75
67.5
7
63.0
122
62.5
000
922
0.5
1.0
Value A5
Value AG
(C) Impact Al
Duke to Market
[c| Impact Als on Markect to Duke
502
30
76.
72
0
80
255
42
030
72
70
0.6
4
60
50-
80
Resivet-30
Reskve--0
ERN-ResNVe-50
20-
ER-ResNee--C (MAF)
E3N Ro Dap
EN Hezalit (Dap
0.0
0. 1.0
0.0
c/a
1.0
Value
Figure 4: Performance evaluation of our proposed MMT-500 with different values of Xiri and Af, in equation 9 on Duke-to-Maine torkee and Market-to- Duke tasks in terms of mAP(%) and (0%) accuracies. Weighting factors Xy,; and X/a balance the contributions between hard and soft pseudo labels. Specifically, only hard labels are adopted when the weighting factors are set to 0.0, and only soft labels are utilized when the weighting factors are set to 1.0.
We utilize weighting factors of Arr = 0.8, X = 0.5 in all our experiments by tuning on Duke- to-Market task with IBN-ResNet-SS backbone and 500 pseudo identities. To further analyse the impact of different Xi,; and A on different tasks, we conduct comparison experiments by varying the value of one parameter and keep the others fixed. Our MMT framework is robust and insensitive to different parameters except when the hard classification loss is eliminated with X 1.0.
The weighting factor of hard and soft triplet losses Xfr:- In Figure (a-b), we investigate the loss is Xi.; and the weight for hard triplet loss (1 X.g). We test our proposed MMT-500 with both ResNet-50 and 1BN-ResNet-50 backbones when Xirs is varying from 0.0, 0.3, 0.5, 0.8 and 1.0. Specifically, the soft softmax- voftmax-triplet loss is removed from the final training objective (equation 9
effect of the weighting factor Xirs in equation 9 where the weight for soft softmax-tripe
when My,, is equal to 0.0, and the hard triplet loss is eliminated when Ns, is set to 1.0. We observe
14

---

Published as a conference paper at ICLR 2020
that the accuracies are almost in direct ratio to the value of Xyr which indicate the effectiveness of our proposed novel soft softmax-triples loss. MMT-500 achieves optimal performances with ResNet-50 backbone on both two tasks when X 1.0. With the backbone of -estet00, MMT-500 obtains the best results with Xyy = 0.8 on Duke-to-Market and X = 0.5 on Market- to-Duke. Despite the performances vary with different values of X all the results by method outperform sotte-of-the-arts significantly.
The weighting factor of hard and soft classification losses Mi. Similar to the comparisons of Nergs we evaluate our proposed MMT-500 framework with different values of Xi which is the weighting factor for hard and soft classification losses in equation As illustrated in Figure(( we observe considerable declines when the hard classification loss equation [ is eliminated with Xid = 1.0. Hard classification loss essential to our proposed framework, which is fully analysed
in Section 4.4 We achieve the optimal performances on both two tasks when X 0.5, while all the experiments with X < outperform sate-of-theeare by large margins.
15


Reviews:
[{"review_id": "rkef5M0RtH", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes an unsupervised domain adaptation method for person re-identification. The proposed method handles noises on pseudo labels created by unsupervised clustering. Two networks are used for training, and the estimated confidences of other models are used for the next training iterations. The temporally average model is used for each network to avoid error amplification. Also, soft softmax-triplet loss is proposed to handle soft labels for triplet loss. \n\nThe handling label noises in unsupervised domain adaptation on person re-identification are new. The proposed model produces very high performance and the contribution for person re-identification community is good. \n\nHowever, I would like to see more insights into the proposed model for the contribution of the general deep learning conference. \n\nFirst, this paper lacks a survey of works on handling label noises. For example, \nB.Han, Q.Yao, X.Yu, G.Niu, M.Xu, W.Hu, I.Tsang, M.Sugiyama, Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels, NeurIPS2018. \n\nI could not fully understand why the temporal averaging of model parameters prevents the two models from being the same. I would like to see a theoretical explanation or experimental evidence for this claim. \n\nThe proposed method also uses noisy hard pseudo labels for training, as shown in Eq.(9). \nWhy are the noisy hard labels used? What is the performance when only soft labels are used for model updates?\n\nIn the experiment, \\lambda^t_{id} = 0.5, \\lambda^t_{tri} = 0.8 are used. Why these parameters are different between softmax and triplet losses?\n\np.1 (Zhang et al., 2018b) and p.5 (Zhang et al., 2019a) are missing in references. \n\n\n", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "Hkg7fUDxFS", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The authors' response addressed my concerns. After reading the reviews and the comments, I choose to stand with the other reviewers.\n\n===================\n\nThis paper uses mean-teacher to ease the noisy pseudo label of clustering methods for domain adaptive Person re-identification task. The authors also propose a variant of triplet loss for soft labels. Experiments show they achieve considerable improvement over state-of-the-art methods.\n\nQuestions:\n1. What's the difference between net 1 and net 2 in Fig. (2)? It seems they are redundant.\n\n2. The results in Table (1) seems to indicate that, in MSMT, if M_t is set to be near the actual identity numbers (1041), the performance will be much better. This makes me suspect that the proposed method benefits from ground truth information of the target domain, which makes the comparison unfair.\n", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I made a quick assessment of this paper.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A"}, "extra_text": {}}, {"review_id": "BJeCW-u0tS", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": null, "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "After reading the reviews and the comments, I confirm my rating.\n\n=================\n\nThe paper proposes an unsupervised framework to address the problem of noisy pseudo labels in clustering-based unsupervised domain adaptation (UDA) for person re-identification. The noise derives from the limited transferability of source-domain features, the unknown number of target-domain identities, and the imperfect results of the clustering algorithm. \n\nThe proposed framework, Mutual Mean-Teaching (MMT), performs pseudo label reﬁnery by optimizing the neural networks under the joint supervisions of off-line reﬁned hard pseudo labels and on-line reﬁned soft pseudo labels. Inspired by the teacher-student approaches (Reference: Tarvainen & Valpola, 2017; Reference: Zhang et al., 2018b), the proposed MMT framework provides robust soft pseudo labels in an on-line peer-teaching manner to simultaneously train two same networks. The networks gradually capture target-domain data distributions and thus reﬁne pseudo labels for better feature learning.\n\nThe main contribution is proposing an unsupervised framework (MMT) capable of tackling the noise problem in state-of-art UDA methods for person re-identification, via producing reliable soft labels in order to achieve better performance. Since the conventional triplet loss cannot properly work with soft labels, a softmax-triplet loss is proposed to enable training with soft triplet labels for mitigating the pseudo label noise.\n\nThe proposed MMT is evaluated on Market1501, DukeMTMC-reID, and MSMT17 datasets with four adaptation tasks: Market-to-Duke, Duke-to-Market, Market-to-MSMT, and Duke-to-MSMT. It outperforms the state-of-the-art methods with significant improvements in terms of Mean average precision (mAP) and Cumulative matching characteristic (CMC). In addition to that, ablation studies conducted to evaluate each component in the proposed MMT framework.", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes an unsupervised framework for domain adaptation in the context of person re-identification to reduce the effect of noisy labels. They use refined soft labels and propose a soft softmax-triplet loss to support learning with these soft labels. 

All reviewers have unanimously agreed to accept the paper and appreciated the comprehensive experiments on four datasets and ablation studies which give some insights about the proposed method. I agree with the assessment of the reviewers and recommend that this paper be accepted.
Decision: Accept (Poster)