Transfer Alignment Network for Double Blind Unsupervised Domain Adaptation
Authors: Huiwen Xu
U Kang
Venue:  (2020)
Keywords: unsupervised domain adaptation
double blind domain adaptation

Abstract: How can we transfer knowledge from a source domain to a target domain when each side cannot observe the data in the other side? The recent state-of-the-art deep architectures show significant performance in classification tasks which highly depend on a large number of training data. In order to resolve the dearth of abundant target labeled data, transfer learning and unsupervised learning leverage data from different sources and unlabeled data as training data, respectively. However, in some practical settings, transferring source data to target domain is restricted due to a privacy policy.

In this paper, we define the problem of unsupervised domain adaptation under double blind constraint, where either the source or the target domain cannot observe the data in the other domain, but data from both domains are used for training. We propose TAN (Transfer Alignment Network for Double Blind Domain Adaptation), an effective method for the problem by aligning source and target domain features. TAN maps the target feature into source feature space so that the classifier learned from the labeled data in the source domain is readily used in the target domain. Extensive experiments show that TAN 1) provides the state-of-the-art accuracy for double blind domain adaptation, and 2) outperforms baselines regardless of the proportion of target domain data in the training data.


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TRANSFER ALIGNMENT NETWORK FOR DOUBLE BLIND UNSUPERVISED DOMAIN ADAPTATION
Anonymous authors Paper under double-blind review
ABSTRACT
How transfer knowledge from source domain to target domain when each side cannot observe the data in the other side? The recent state-of-the- art deep architectures show significant performance in classification tasks which highly depend on large number of training data. In order to resolve the dearth of abundant target labeled data, transfer learning and unsupervised learning lever- age data from different sources and unlabeled data as training data, respectively. However, in some practical settings, transferring source data target domain restricted due to privacy policy.
In this paper, we define the problem of unsupervised domain adaptation under double blind constraint, where either the source or the target domain cannot ob- serve the data in the other domain, but data from both domains are used for train- ing. We propose TAN (Transfer Alignment Network for Double Blind Domain Adaptation), an effective method for the problem by aligning source and target domain features. TAN maps the target feature into source feature space so that the classifier learned from the labeled data in the source domain is readily used in the target domain. Extensive experiments show that TAN 1) provides the state-of- the-art accuracy for double blind domain adaptation, and outperforms baselines regardless of the proportion of target domain data in the training data.
INTRODUCTION
How can we minimize data distribution gap between source and target domains under constraint of data visibility, such that each side cannot observe the data in the other side? Recent state-of-the-a deep learning algorithms perform well on test data when given abundant labeled training data. How- ever, in real applications. collecting large amount of labeled data needs tedious labor work while unlabeled data are abundant. Given a certain number of unlabeled data from target domain, unsu- pervised transfer learning exploits the knowledge from a related source domain, which has enough unlabeled data accompanying small number of labeled data, to improve the model performance on target domain. Since the source domain is related to but different from the target domain, data distribution shift exists between the two domains. Domain adaptation attempts to reduce the data distribution shift by effectively transferring knowledge from the source the target domains. Recent state-of-the-at methods focus on digging out domain-invarian features, and learn more transferable representations to improve transferability. Cao et al. (2018) proposed transfer learner by extracting invariant feature representations while Long et al. (2018) applied generative adversarial model and generated feature extractor confuse domain classifier. The premise of applying such methods is that source and target data are visible simultaneously. However, in real-world settings including hospital cases, it not allowed access source and target data simultaneously for privacy reason.
In this paper. we define the problem of unsupervised domain adaptation under double blind con- straint which restricts mutual accesses between source and target domains. As shown in Figure 1, general domain adaptation can leverage source and target data simultaneously, but domain adap- tation under double blind constraint cannot utilize the two data simultancously because the re- striction data accessibility. Due to the constraint, existing methods domain adaptation are not suitable for the new problem. We are exposed to two challenges: (1) since source and target data not visible simultaneously, we have way of knowing how much the data distribution shifts between the two domains, and (2) the target data cannot be used to train a meaningful classifier due to the missing label.

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Training
Test
Uulabeled & Labeled Training (Source Domain)
Unlabeled Training Data (Target Domain)
Unlabeled Test Data (Target Domain)
Domain Adaptation
Domain Adaptation Model
Model
Domain adaptation without double blind constraint
Training
Test
Unlabeled & Laboled Training Data (Source Domain)
Unlabeled Truining (Target Domain)
Unlaheled Test Data (Target Domain)
(1)
(3)
Domain
Domain
Domain
Adaptation
Adaptation
Adaptation
Model
Model
Model
(b) Domain adaptation under double blind constraint
Figure 1: Comparison between domain adaptation with or without double blind constraint. (a) Domain adaptation without double blind constraint leverages source and target data simultaneously at training time. (b) Domain adaptation with double blind constraint cannot feed data from source and target domains simultaneously into model at training time.
To address the challenges, we propose TAN (Transfer Alignment Network for Double Blind Do- main Adaptation), an effective method for double blind domain adaptation which resolves the data distribution shift problem by aligning source and target feature spaces. TAN maps target features into source feature space in order to utilize the classifier trained source domain to target data. The experimental results show the superior performance of TAN.
Our contributions in this paper are the followings:
Problem Definition. We propose new problem of unsupervised domain adaptation under double blind constraint, which involves data inaccessibility during training process. Method. We introduce simple but effective model TAN which aligns data distribution of source and target domains.
Experiments. Extensive experiments real-world multivariate datasets show that TAN provides the state-of-the-art performance.
In the rest of this paper, we describe preliminaries, proposed method, experimental results, and related works, and conclude in the end.
PRELIMINARIES
In this section, we provide brief description of autoencoder pre-training, and transfer learning with fine-tuning.
2.1 AUTOENCODER PRE-TRAINING
In order to leverage unlabeled data in neural network training, the most common approach is to initialize parameters by weights pre-trained using autoencoder (Erhan et al. (2010)). Autoen- coder (Bengio et al. (2009)) is trained to reconstruct feature vector from code constrained either in dimensionality sparsity. After training, the encoded vector from each input feature has enough in- formation to reconstruct the original feature, and represents the hidden structure in the input feature space.
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Autoencoder is composed of two parts: encoder and decoder. Encoder maps the input feature into a constrained code, and decoder maps the code back to feature reconstruction. Given input fea- ture vector z, encoder maps to code h = f(x). The decoder g maps h to reconstruction = g(h) = g(f(x)) Both encoder and decoder are defined as neural networks, with iterative applications of affine transformation and activation function. Then the model is trained to mini- mize the reconstruction error for each data point de in dataset. The objective function is defined as ming >; ||; æill² where I; is a reconstruction (x:)) from input æ; and 0 denotes neural network parameters for encoder f and decoder
Given large amount of unlabeled data and few labeled data, we use code vector f(z) from encoder as input in a classifier to effectively train it using the few labeled data. If the code vector has enough information to reconstruct the input feature vector, then it would contain enough information for the classifier as well.
2.2 TRANSFER LEARNING WITH FINE-TUNING
Transfer learning with fine-tuning approach is simple and easy to apply in neural network training. In the training process, a neural network classifier learns useful intermediate hidden units (Bengio (2012)). These hidden units provide enough information for simple classifier achieve the state- of-the-art performance not only in the same task (Sharif Razavian et al. (2014)) but also in related tasks (Sermanet al. (2013).. This property has been exploited to neural network trained on a source domain as initialization for further fine-tuning on a target domain. With careful analy- sis (Yosinski et al. (2014)), has been shown that transferring pre-trained neural network features improves performance. Fine-tuning is important to transform source-specific features into target- specific features and to alleviate layer co-adaptation problem. It has been a common practice in neural network transfer learning to adapt parameters that are pre-trained from source domain and fine-tune the model on a target domain.
3 PROPOSED METHOD
In this section, we precisely define the problem of double blind unsupervised domain adaptation and its challenges. Then we describe our method TAN in detail. Figure 2 illustrates the architecture of our method in detail and Table provides the definitions of symbols used.
3.1 PROBLEM DEFINITION AND CHALLENGES
In unsupervised domain adaptation, a source domain has abundant unlabeled data X {>"")) 1==1 and small number of labeled data X {(a]' . Di2.. A target domain has only limited number of unlabeled data X = {xx ) Note that the number n2, of unlabeled sam- ples from the source domain is larger than the number n, from the target domain. Both of source and target data contain the same number of classes. The source and the target data are sampled from distributions p and q, respectively, where p q. Our goal is to learn classifier which per- forms well in target classification task under double blind constraint, where each domain cannot observe the data in the other domain, by leveraging both data in the source and target domains. Note that this problem of double blind domain adaptation is slightly different from blind domain adapta- tion (Uzair& & Mian (2017)) since the former uses the data from both source and target domains for training. while the latter uses the data only from the source domain for training.
Due to the double blind constraint, 1) existing domain adaptation models cannot be implemented directly, and 2) the distribution discrepancy between source and target data is not measured easily. Another complication is that the target data have no labels, which makes it impossible train classifier if only using the target data without accessing the labeled data at the source domain. We need a novel method to match feature distributions of source and target domains.
3.2 TRANSFER ALIGNMENT NETWORK FOR DOUBLE BLIND DOMAIN ADAPTATION
The target data cannot be used to generate a classifier due to missing labels, which forces us to utilize the source domain data train classifier and make use of it in the target domain. Even
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Table I: Table of symbols.
Symbol
Terminology
Description
X
source unlabeled data
data instances without labels in source domain data instances with labels in source domain data instances without labels in target domain neural network that encodes input into code neural network that decodes code vector imput reconstruction neural network that aligns target code vector source code vector neural network that classifies code vector
X
source labeled data
Xt.
target unlabeled data encoder
fencode
fdecode
decoder
falign
transfer aligner
JdassiJy
classifier
S.eenodee
source encoder
encoder after Step training classifier after the Step 2 training encoder after Step training space code from source encoder space code from target encoder
Ss,classify
source classifier
ft.encode
target encoder
Ja X)
source code space
target code space
Training
Step 4 (target, unlabeled) Encoder from Step
Step (source, unlabeled)
Step 2 (source, labeled)
Step (target, unlabeled)
xaa Autenender
x
xd
MLP
Autoencoder
Minimize
Encoder from Step 3
Step
yn))) ; source labeled data
[0000)
source unlabeled data
Test
x/u
target unlabeled data
source latent variable
target Intent variable
aligned latent variable
Figure 2: Structure of TAN. In training step 1, we train an autoencoder with unlabeled source instances. In training step we train a stack of classifier and the encoder using labeled source instances. Then, after transferring the model to target domain, we fine-tune the autoencoder on unlabeled target instances in training step 3. In training step 4, transfer aligner (in red color) is trained to map the target feature space back to the source feature space using unlabeled target instances. At test time, each instance goes through the final model consisting of the fine-tuned autoencoder and the transfer aligner trained at step 4, and the source classifier trained at step 2.
though there are a large amount of data in the source domain, most of them are unlabeled: it is hard train good classifier using only the small amount of labeled data in the source domain. Thus, we use the source unlabeled data to train an autoencoder to extract meaningful features. Then, the source labeled data go through the autoencoder become valuable features, and we utilize the features with labels to train a classifier. Due to the double blind constraint, we can only transfer the model trained the source domain, not data. After the model transferred the target domain, we finetune the autoencoder with the target unlabeled data. Because of data distribution discrepancy between the source and the target data, directly feeding the extracted target domain features into the pretrained classifier will seriously damage the accuracy. To tackle this problem, we add and train transfer adaptation layers, which we call "transfer aligner", between the autoencoder and the classifier using only the target unlabeled data. After being passed through the transfer aligner, the target domain features have a distribution as close to the source distribution, so that we can use the classifier trained in the source domain classify the target unlabeled data.

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Algorithm TAN (Transfer Alignment Network for Double Blind Domain Adaptation)
Input: source labeled data X=*,, source unlabeled data X and target unlabeled data X
Output: encoder for autoencoder fencode. transfer aligner falign and classifier fclassify
1: (Step 1) Train fencode and fdecode on X= with L2 reconstruction loss
2: (Step 2) Train Sencnde. and fylassif on X with cross-entropy classification loss Take snapshot Sencode. as Jssencode
3:
4: (Step 3) Train fencode and fdecode on X with L2 reconstruction loss
5: Take snapshot f fencode as ft.encode
6: (Step 4) falign = TTrain.Alggue (faeerrsssseee St.encodes X in Algorithm
Algorithm Train Transfer Aligner
Input: source encoder Js,encodos target encoder Jttenendes and target unlabeled data Xt,
Output:
transfer aligner falign
1: Initialize fatign with random weights
2: Calculate source code z = fs,encode (z;) and target code of ft,encode (24) for each data instance I; E Xt
3: Train falign to minimize E || 22 using gradient based optimization, where 2, = (alig((2))
The whole architecture is simple but effective, consisting of encoder, transfer aligner, and multilayer perceptron. Figure 2 shows the training and the test steps of TAN. We train autoencoder with unlabeled source instances in Step 1. The blue rectangle encoder. In Step 2, utilize the encoder part trained from Step by adding multilayer perceptron on top of the code vector and the model trained with labeled source instances. In Step 3, we transfer the model the target domain, but only fine-tune the autoencoder using unlabeled target instances. In Step 4, the transfer aligner (in red) is trained to directly map the target code vector into source code space using the unlabeled target instances. We train the transfer aligner falign map the code of the target encoder ft.encode to the code of the source encoder fa fenenc for the target unlabeled data tt. As shown Figure the encoder, the transfer aligner, and the classifier are stacked together for the complete model. We test the final model with unlabeled target test data. The whole algorithms are described in Algorithms and We further elaborate each step as follows:
feature vector extracted from the
Step 1. The encoder fencode and decoder fdecode are trained on source unlabeled data X** The model is trained minimize the following autoencoder reconstruction objective function.
Σ ||= - fdecode (fencode (x:)))l²
2,E
We train classifier feiassify on top of the previous encoder fencode on source labeled data * with fine-tuning. The training process minimizes the following cross- entropy objective function.
Step 2.
Σ Xod
Σv, ys log fciassify
(eencotte CesiaassifyySennttee (x;)))
We call the encoder fencode and the classifier falassify this step source encoder fa fanenodee and source classifier fe.classify. respectively. Snapshot of current parameters of the source encoder fs,encode is stored separately.
Step 3. The encoder fencode and the decoder fdecode is fine-tuned on target unlabeled data X again with the following standard autoencoder reconstruction objective function.
Σ t.
||, - -fecodde (x:))))
We call the encoder fencode with fine-tuned parameters at this step target encoder fr.encode- Snapshot of current parameters of target encoder fe.ecodee is again stored sepa- rately.
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Table Statistic of the datasets.
HIGGS
HEPMASS
SUSY
Sensorless'
Gas
Number of instances
11,000,000
10,500,000
5,000,000
58,509
13,900
Feature dimensions Number of classes
28
28
18
49
128
2
2
11 6
Step 4. The transfer aligner fatign is trained to map target code ft.encode (x) to source
code fs.encode( the target unlabeled data X The objective function is L2 distance between the source code fa,encode (Iz) and the mapped target code falign (ft,,ncode (x;))) (2;)):
Σ
|1ss,encode (2;) - falign (ft, (#x)))c
(4)
TEX
this step, only the transfer aligner fatign (the red part) is trained, while the encoders are fixed.
4 EXPERIMENTS
We present experimental results to answer the following questions.
Q1. (Predictive Performance) What are the accuracies of TAN and competitors for double
blind domain adaptation?
Q2. (Effect of Transfer Aligner Training) Does the benefit of transfer aligner come from
alignment of features rather than deployment of more layers? Q3. (Effect Amount of Target Data) Is the model affected by the proportion of source and target data?
4.1 EXPERIMENTAL SETTING
Datasets. We conduct experiments on five multivariate datasets summarized in Table 2: HIGGS!, HEPMASS?, SUSY Sensorless", and Gas HEPMASS, HIGGS and SUSY are sensor observa- tions of particle collision experiments where labels are binary and denote the actual generation of the particles of interest. Sensorless and Gas are also sensor observations where labels denote the diagnosis of motor status and the type of gas in chemical reaction, respectively.
Data preprocessing. order to perform the domain adaptation experiments, we require a dataset with source/target labeled/unlabeled instances. We divide the total training data into 10 clusters using K-means so that instances in each cluster are closer to each other compared to those in other clusters. We first select random point Tr. Then, we set the farthest point с₁ from I, as the first centroid. The 2nd centroid 02 is determined to be the point with the farthest distance to C1 The keth centroid c for & = 4, ..., 10 is determined the point with the farthest shortest distance to the existing centroids:
= arg max,, min( [r; ei 21112,... ... ||; cx-1112), The found 10 clusters can be divided into source and target data with 9:1 ratio, which leads to 10 possible combinations of source and target data. We select the cluster with the largest H-divergence to other clusters as the target data. For efficient computation of H-divergence, we approximate it using Proxy A-distance d = 2(1 2c) where the error of correctly discriminating source and target domains (Ben- David et al. (2010)) using SVM. Because target data instances are sampled from only one cluster, source and target datasets have different feature distribution as intended. The final data ratio Source Unlabeled:Source Labeled:Source Test:Target Unlabeled:Targer test = 80. 18.19:::::
Model settings. We examine the influence of transfer aligner for domain adaptation with the fol- lowing architecture: 80-40-20-10 autoencoder, 30-80-150 transfer aligner, and 300-300-300-30--
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Table 3: Test accuracy of TAN and baseline models (%) TAN gives the best accuracy.
Methods
HIGGS
HEPMASS
SUSY
Sensorless
Gas
S(UL)
83.74 + 0.62 73.05 = 0.77 84.99 + 0.83 75.38 + 0.99 70.53 + 0.54
S(UL)-T(U)
59.97 + 0.22 76.18 + 0.68 48. 18 + 0.63 76.00 + 1.26 68. 68.28 + 1.08
TAN
92.74 0.37 79.95 0.84 88.86 + 0.26 78.02 = 1.37 74.63 + 1.49
Table 4: Effect of transfer aligner in test accuracy (%). The superior accuracy of TAN comes from its transfer aligner, not from increased number of parameters.
Methods
HIGGS HEPMASS
SUSY
Sensorless
Gas
S(UL)-T(U)-Larr
87.05 + 0.39 76.23 + 1.23 84.42 + 0.42 77.12 + 1.89 72.29 + 1.34
TAN
92.74 + 0.37 79.95 + 0.84 88.86 + 0.26 78.02 = 1.37 74.63 + 1.49
300 classifier. We apply ReLU activation function for all hidden layers(inctuding hidden layers in encoder, decoder, transfer aligner, and mlp) and softmax function for the prediction layer while en- coding layer (z" and z' in Figure 2) and reconstruction layer (z and z in Figure 2) have no activation function. We implement all methods based on the Tensorflow framework. All parameters are randomly initialized in the source domain, and the model is trained from scratch while we utilize the pretrained model from the source domain to fine-tune it in the target domain.
Competitors. We compare TAN with the following baselines.
S(UL): a stack of an encoder and a neural network classifier trained using source data (unlabeled data and labeled data, respectively) and tested on target data without finetuning. This corresponds to using only Steps 1 and 2 of Figure 2 in training.
S(UL)-T(U): this model retrains the S(UL) model with the target unlabeled data. This corresponds to using Steps 1, 2, and 3 of Figure 2 in training. In another point of view, this model is similar to TAN, but without the transfer aligner in Step
T(U-a-T( this model is similar to S(UL)-T(U), but contains more layers and parameters MLP of Step so that the total number of parameters is close to that of TAN.
4.2 PREDICTIVE PERFORMANCE (Q1)
The predictive accuracy on target test set is summarized in Table 3. For all the datasets, TAN achieves the best predictive performance outperforming the baseline methods. The accuracy boost of TAN compared to naive supervised learning (S(UL)) is up to 9.0%. This shows that using target data via transfer learning, even though they are unlabeled, helps improve the accuracy. Also, TAN shows much better accuracy than S(UL)-T(U) which further finetunes the model with the target unlabeled data. The reason is that S(UL)-T(U) causes the mismatch of distributions in the source and the target feature spaces. On the other hand, TAN uses the transfer alignment layer for matching the distributions which leads to superior performance in double blind domain adaptation.
4.3 EFFECT OF TRANSFER ALIGNER TRAINING (Q2)
We examine the effect of transfer aligner in TAN, Specifically, we verify the hypothesis that the su- perior performance of TAN does come from its mere increase in layer size. To verify it, we train a competitor ((UL)-T(U)-Larg which uses more layers compared to S(UL)-T(U) so that the same number of parameters is the same that of TAN. Table 4 shows TAN consistently outperforms S(UL))T(U)-Large with better accuracy up to 5.7%. This result verifies that the superior accuracy of TAN comes from its careful design of transfer aligner, not from its increase in the number of parameters.
4.4 EFFECT OF THE AMOUNT OF TARGET DATA (Q3)
We vary the ratio of target unlabeled data to total data by sampling the target unlabeled data and inspect the performance of TAN and competitors. For each dataset, we set the proportion of target

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100
S(U))
SUULY T(U)
TAN
100
S(UL)
SUULY T(U)
FAN
100
SUL)
S(UL) T(U)
TAN
Accuracy
Accuracy
Accurncy
80
80
80
60
60
60
40
40
9%
6%
3%
1%
9/
6%
3%
1%
9%
6%
3
1%
Ratio of target unlabeled data
Ratio of turget unlabeled data
Ratio
target unlabeled data
(a) HIGGS
(b) HEPMASS
(c) SUSY
100
S(UL)
SUL))-(UU)
TAN
SUL
S(UL)-T(U)
TAN
Accuracy
Accuracy
50
50
AAND
25
25
3%
1%
6%
3%
Ratio of target unlaheled data
Ratio of target unlaheled data
(d) Sensorless
(c) Gas
Test accuracy of TAN and baseline models for varying ratios of target unlabeled data. TAN gives the best accuracy regardless the ratio target unlabeled data all datasets.
Figure 3:
unlabeled data 9%, 6%. 3%, 1% of the total data from both domains, to ensure that the numbers of target unlabeled instances are at least 100. As shown in Figure 3, TAN consistently outperforms baselines in all proportions and datasets, showing stability of transfer aligner in method.
RELATED WORK
Training deep neural networks requires massive amount of dataset; however, task task-specific labeled dataset is often scarce. A common approach for the problem is to extract more information from other sources of data: unlabeled datasets from the same domain, or datasets from other domains.
Transfer learning (Pan & Yang (2010); Weiss et al. (2016)) aims to transfer a machine learning model source domain to a different target domain having different distribution from that of its training data. Generalizable knowledge learned from the source domain is leveraged to learn better model in the target domain. There have been many researches applying transfer learning to achieve the state-of-the soffthe-artt performance in both computer vision (Aytar & Zisserman (201 1); Girshick al. (2014); Long et al. (201 Ren et al. (2015) and natural language processing (Collobert et al. (201) 1); Glorot et al. (201 I); Chen et al. (2012); Xiao & Guo (2013)) domains.
Existing methods on deep transfer learning frequently minimize feature distribution discrepancy be- tween source and target domains (Tzeng al. (2014: 2015; 2017); Long al. (2015b; 2016; 2017); Ganin & Lempitsky (2015); Ganin et al. (2016); Bousmalis et al. (2016); Zhang et al. (2015)). These methods utilize source and target data together the training process calculate and optimize fea- ture distribution discrepancy. Other works on transfer learning iteratively assign and are trained on aseudo-labels for unlabeled source data and target data, respectively (Chen al. (201 1); Rohrbach et al. (2013), 3); Lee (2013); Khamis & Lampert (2014); Sener et al. (2016); Ma et al. (2017); Saito (2017); Cao et al. (2018)). These methods iteratively fine-tune models on both source and target data to provide better pscudo-labels. Most recently proposed works (Kumar et al. (2017); Dai et al. (2017): Miyato et al. (2017); Santos et al. (2017); Long et al. (2018)) exploit adversarial training learn the structure in feature space. These studies measure the mismatch in data distribu- tion by utilizing both source and target data. Unlike the aforementioned approaches, our goal is to perform domain adaptation where both of source and target data not visible simultaneously.
Although existing blind domain adaptation method (Lampert (2014); Uzair & Mian (2017)) does not observe target data when using source data for training, they do not the target data for training.
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On the other hand, our goal is to solve double blind domain adaptation problem where both of source and target data are used for training, even though they are not visible simultaneously.
6 CONCLUSION
We propose TAN, simple and novel deep transfer learning method for double blind domain adap- tation. Since the target domain data have no labels, which makes it infeasible train a classifier in the target domain, we utilize the classifier trained on source domain to discriminate the target data. We minimize the domain discrepancy between source and target feature spaces, in order to make the classifier have better performance on the target domain data. However, source and target data cannot be visible simultaneously under the double blind constraint setting, which means general domain adaptation methods cannot be applied to the problem. To address this issue, TAN exploits transfer aligner that maps the target feature space to the source feature space without accessing any source data. Extensive experiments show that TAN provides the best transfer accuracy under double blind constraint. Future works include extending TAN to heterogeneous transfer learning.
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Under review as a conference paper at ICLR 2020
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Reviews:
[{"review_id": "r1xX5KR0OB", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 1, "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": "\n\n###Summary###\n\nThis paper tackles the transfer learning problem with the double-blind unsupervised domain adaptation, where either the source or the target domain cannot observe the data in the other domain, but data from both domains are used for training. The high-level intuition of this paper is based on the observation that in some practical settings, the transferring source data to the target domain is restricted due to the privacy policy. The goal is to learn a classifier which performs well in target classification task under double-blind constraint. \n\nThe setting of this paper is slightly different from the conventional domain adaptation. In this paper, the source domain has abundant unlabeled data and a small number of labeled data. The target domain only contains a limited number of unlabeled data.\n\nThe paper proposes a transfer alignment network (TAN) which comprises two autoencoders, one trained on the source domain and one trained on the target domain. In the domain adaptation phase, the model leverages an aligner to transfer the output of the target encoder to an aligned latent variable.  The aligner is trained to map the target code to source code on the target unlabeled data. The objective function is L2 distance between the source code and the mapped target code. \n\nThe whole pipeline is trained with four steps:\n1) The source encoder and source decoder are trained with L2 reconstruction loss.\n2) The source encoder and source classifier are trained with cross-entropy classification loss.\n3) The target encoder and target decoder are trained with L2 reconstruction loss.\n4) Train the aligner to map target code to source code on target unlabeled data with L2 distance loss.\n\nThe paper proposes to compare the TAN with three baselines: S(UL):  a stack of encoder and a neural network classifier trained using source data and tested on target data without finetuning. S(UL)-T(U): a model retrains the S(UL) with the target unlabeled data. S(UL)-T(U)-Large: a model which is similar to S(UL)-T(U) but contains more layers and parameters in MLP.\n\nThe experiments are performed on five multivariate datasets: HIGGS, HEPMASS, SUSY, Sensorless, and Gas. \n\n\n### Novelty ###\n\nThe experimental setting proposed in this paper is interesting. However, the proposed model is trivial. The TAN model is composed of autoencoders and aligner. The training losses in the framework are L2 reconstruction loss and L2 distance loss. Thus, the novelty of this paper is incremental.\n\nThe experimental results in this paper are weak. First of all, the datasets used in this paper are not standard benchmarks. Secondly, the baselines in this paper are too trivial.   \n\n\n###Clarity###\n\nOverall, the paper is well organized and logically clear. The images are well-presented and well-explained by the captions and the text. \n\n###Pros###\n\n1) The paper proposes an interesting transfer learning framework where either the source or the target domain cannot observe the data in the other domain. \n\n2) The paper is applicable to many practical scenarios since the data privacy in the real-world application is critical. \n\n3) The paper is overall well-organized. The claims of the paper are verified by the experimental results.\n\n###Cons###\n\n1) The paper proposes double-blind unsupervised domain adaptation as accessing the source and target domains is restricted in some practical settings. However, the source and target domain share the models trained on themselves, as well as the features extracted from the source domain and target domain data. The information about the original data can be recovered with the shared features and weights, which violates the settings proposed in this paper. \n\n2) The main issue of this paper is the novelty is incremental. The proposed model is trivial as it only contains the auto-encoders and L2 loss. \n\n3) The experimental part of this paper is weak. The datasets used in this paper are not the standard domain adaptation benchmark. It would be nice to see how does the proposed model work on standard domain adaptation benchmarks such as Office31, VisDA, Office-Home, DomainNet, etc.\n\nVisDA: The Visual Domain Adaptation Challenge\nhttps://arxiv.org/pdf/1710.06924.pdf\nOffice-Home : Deep Hashing Network for Unsupervised Domain Adaptation\nhttp://hemanthdv.org/OfficeHome-Dataset/\n\nThe baselines used in this paper is also trivial. It is desirable to compare the proposed method with state-of-the-art domain adaptation methods.\n\nBased on the summary, cons, and pros, the current rating I am giving now is \"reject\". I would like to discuss the final rating with other reviewers, ACs.\n\n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "N/A"}, "extra_text": {}}, {"review_id": "Syl16_j5tS", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 1, "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 a TAN model for double blind UDA problem, which supposes that partial data drawn from source domain is unlabeled and the target domain is completely unlabeled.\n\nActually, blind domain adaptation has been proposed several years ago. The double blind domain adaptation has no signficant difference.\n\nFrom the model, I could not observe the technical novelty, although the authors focus on the \"Aligner\". Actually, autoencoder based domain adaptation has also been proposed for several years.\n\nThe aligner can actually be removed, by jointly training a domain-shared encoder in Step 3 when the unlabeled target data is used for training.\nIn other words, step 4 can be removed and in test stage, the output of encoder means the domain aligned feature representation. \n\nAdditionally, the source classifier is trained independently from the unlabeled target data. Although the domain aligned feature representation can be learned, it is still risky to directly apply the source classifier for unlabeled target data. Therefore, I suggest a safe strategy to train the source classifier and the domain aligned feature represenation module by jointly feeding the labeled source and unlabeled target data into the Step 2. That is, the step 3 can be integrated into step 2 for safer training.\n\nI also have concerns on the experimental datasets. Why not use the benchmark visual datasets?\n\nThe proposed model lacks of comparisons with many state-of-the-art models.\n", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly."}, "extra_text": {}}, {"review_id": "BJlfihuRYH", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 1, "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": "In this paper, the authors claimed to address a new domain adaptation setting under double blind constraint, to meet the privacy requirement. Though the problem itself seems real and interesting, the solution in this work makes the problem quite trivial. In fact, any other domain adaptation method can be applied to address the problem, while the authors even did not compare with existing UDA methods which can be intuitively adapted.\n\nPros:\n-\tThe problem that this work aims to address, i.e., domain adaptation under privacy constraint, seems reasonable and important.\n\nCons:\n-\tThe authors proposed a quite trivial solution to solve this problem, which in turn makes all existing UDA adaptation methods off-the-shelf. For example, the most widely accepted DA algorithms, such as MMD and DANN, can be adapted to minimize the distance between the target encoder and the source encoder by training the weights for the target encoder. In this case, the aligner introducing more parameters is not even necessary. \n-\tThe results are not promising and inspiring. S(UL), directly applying a model trained on a source dataset, has already achieved as competent results as TAN. \n-\tThe datasets used are at a toy level from UCI. More profound discoveries are expected on DA benchmarks. \n-\tThe paper needs significant proof-reading, as there are many grammatical errors and typos.\n", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper tackles the problem of how to adapt a model from a source to a target domain when both data is not available simultaneously (even unlabeled) to a single learner. This is of relevance for certain privacy preserving applications where one setting would like to benefit from information learned in a related setting but due to various factors may not be willing to directly share data. The proposed solution is a transfer alignment network (TAN) which consists of two autoencoders (each trained independently on the source and the target) and an aligner which has the task of mapping the latent codes of one domain to the other. 

All three reviewers expressed concerns for this submission. Of greatest concern was the experimental setting. The datasets chosen were non-standard and there was no prior work to compare against directly so the results presented are difficult to contextualize. The authors have responded to this concern by specifying the existing domain adaptation benchmarks are more challenging and require more complex architectures to handle the “more complex data manifolds”. The fact that existing benchmark datasets may be more complex the the dataset explored in this work is a concern. The authors should take care to clarify whether their proposed solution may only be applicable to specific types of data. In addition, the authors claim to address a new problem setting and therefore cannot compare directly to existing work. One suggestion is if using new data, report performance of existing work under the standard setting to give readers some grounding for the privacy preserving setting. Another option would be to provide scaffold results in the standard UDA setting but with frozen feature spaces. Another option would be to ablate the choice of L2 loss for learning the transformer and instead train using an adversarial loss, L1 loss etc. There are many ways the authors could both explore a new problem statement and provide convincing experimental evidence for their solution. The AC encourages the authors to revise their manuscript, paying special attention to clarity and experimental details in order to further justify their proposed work. 
Decision: Reject