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Title: Open-Set Domain Adaptation with Category-Agnostic Clusters

Paper content:
Unsupervised domain adaptation has received significant attention in recent years.
Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories.
In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class).
The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source.
In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain.
Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) --- a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain.
These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios.
Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain.
A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample.
Furthermore, SE-CC enhances the learnt representation with mutual information maximization.
Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches.
Convolutional Neural Networks (CNNs) have driven vision technologies to reach new state-ofthe-arts.
The achievements, nevertheless, are on the assumption that large quantities of annotated data are accessible for model training.
The assumption becomes impractical when cost-expensive and labor-intensive manual labeling is required.
An alternative is to recycle off-the-shelf learnt knowledge/models in source domain for new domain(s).
Unfortunately, the performance often drops significantly on a new domain, a phenomenon known as "domain shift."
One feasible way to alleviate this problem is to capitalize on unsupervised domain adaptation, which leverages labeled source samples and unlabeled target samples to generalize a target model.
One of the most critical limitations is that most existing models simply align data distributions between source and target domains.
As a consequence, these models are only applicable in closed-set scenario (Figure 1(a) ) under the unrealistic assumption that both domains should share exactly the same set of categories.
This adversely hinders the generalization of these models in open-set scenario to distinguish target samples of unknown class (unseen in source domain) from the target samples of known classes (seen in source domain).
The difficulty of open-set domain adaptation mainly originates from two aspects: 1) how to distinguish the unknown target samples from known ones while classifying the known target samples correctly?
2) how to learn a hybrid network for both closed-set and open-set domain adaptation?
One straightforward way (Figure 1(b
) ) to alleviate the first issue is by employing an additional binary classifier for assigning known/unknown label to each target sample Panareda Busto & Gall (2017) . All
the unknown target samples are further taken as outlier and will be discarded during the adaptation from source to target. As
the unknown target samples are holistically grouped as one generic class, the inherent data structure is not fully exploited. In
the case when the distribution of these target samples is diverse or the semantic labels between known and unknown classes are ambiguous, the performance of binary classification is suboptimal. Instead
, we novelly perform clustering over all unlabeled target samples to explicitly model the diverse semantics of both known and unknown classes in target domain, as depicted in Figure 1 (c). All
target
samples are firstly decomposed into clusters, and the learnt clusters, though category-agnostic, convey the discriminative knowledge of unknown and known classes specific to target domain. As such, by
further steering domain adaptation with category-agnostic clusters, the learnt representations are expected to be domain-invariant for known classes, and discriminative for unknown and known classes in target domain. To address
the second issue, we remould Self-Ensembling French et al. (2018) with an additional clustering branch to estimate the assignment distribution over all clusters for each target sample, which in turn refines the learnt representations to preserve inherent structure of target domain.
To this end, we present a new Self-Ensembling with Category-agnostic Clusters (SE-CC), as shown in Figure 2 .
Specifically, clustering is firstly implemented to decompose all the target samples into a set of category-agnostic clusters.
The underlying structure of each target sample is thus formulated as its inherent cluster distribution over all clusters, which is initially obtained by utilizing a softmax over the cosine similarities between this sample and each cluster centroid.
With this, an additional clustering branch is integrated into student model of Self-Ensembling to predict the cluster assignment distribution of each target sample.
For each target sample, the KL-divergence is exploited to model the mismatch between its estimated cluster assignment distribution and the inherent cluster distribution.
By minimizing the KL-divergence, the learnt feature is enforced to preserve the underlying data structure in target domain.
Moreover, we uniquely maximize the mutual information among the input intermediate feature map, the output classification distribution and cluster assignment distribution of target sample in student to further enhance the learnt feature representation.
The whole SE-CC framework is jointly optimized.
We have presented Self-Ensembling with Category-agnostic Clusters (SE-CC), which exploits the category-agnostic clusters in target domain for domain adaptation in both open-set and closed-set scenarios.
Particularly, we study the problem from the viewpoint of how to separate unknown target samples from known ones and how to learn a hybrid network that nicely integrates category-agnostic clusters into Self-Ensembling.
We initially perform clustering to decompose all target samples into a set of category-agnostic clusters.
Next, an additional clustering branch is integrated into student model to align the estimated cluster assignment distribution to the inherent cluster distribution implicit in category-agnostic clusters.
That enforces the learnt feature to preserve the underlying data structure in target domain.
Moreover, the mutual information among the input feature, the outputs of classification and clustering branches is exploited to further enhance the learnt feature.
Experiments conducted on Office and VisDA for both open-set and closed-set adaptation tasks verify our proposal.
Performance improvements are observed when comparing to state-of-the-art techniques.

assistant: text: We present a new design, i.e., Self-Ensembling with Category-agnostic Clusters, for both closed-set and open-set domain adaptation.