# OneRing: A Simple Method for Source-free Open-partial Domain Adaptation

Shiqi Yang, Yaxing Wang${}^{(\textrm{{\char 0\relax}})}$, Kai Wang, Shangling
Jui  Joost van de Weijer S. Yang, Kai Wang and J. van de Weijer are with the
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona 08193,
Spain.
E-mail<EMAIL_ADDRESS>Y. Wang, College of Computer Science,
Nankai University, China<EMAIL_ADDRESS>S. Jui is with Huawei
Kirin Solution, Shanghai, China<EMAIL_ADDRESS>Manuscript
received April 19, 2005; revised August 26, 2015.

###### Abstract

In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-
OPDA), which addresses the situation where there exist both domain and
category shifts between source and target domains. Under the SF-OPDA setting,
which aims to address data privacy concerns, the model cannot access source
data anymore during target adaptation. We propose a novel training scheme to
learn a ($n$+1)-way classifier to predict the $n$ source classes and the
unknown class, where samples of only known source categories are available for
training. Furthermore, for target adaptation, we simply adopt a weighted
entropy minimization to adapt the source pretrained model to the unlabeled
target domain without source data. In experiments, we show our simple method
surpasses current OPDA approaches which demand source data during adaptation.
When augmented with a closed-set domain adaptation approach during target
adaptation, our source-free method further outperforms the current state-of-
the-art OPDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA
respectively.

###### Index Terms:

Source-free Domain Adaptation, Open-partial Domain Adaptation

##  1 Introduction

Modern deep learning models excel at closed-set recognition tasks across
various computer vision application areas. However, there are several
inevitable obstacles on the path to deploying these methods to challenging
real-world environments. As there may be 1) some unseen categories, or 2) a
distributional shift between training and testing data. The first problem is
usually defined as open-set recognition (OSR) [6, 14, 36, 51, 65, 49, 55]
where the model should be able to distinguish samples from seen or unseen
categories. The second problem is mostly investigated in domain generalization
(DG) [46, 41, 56, 15, 57] and domain adaptation (DA) [31, 32, 34, 53, 66, 7,
26, 9, 52, 8]. DG aims to tackle the domain shift problem in the absence of
target domains, while DA seeks to transfer knowledge from labeled source
domains to unlabeled target domains while training on both labeled source and
unlabeled target data. In recent years, several works introduce open-set
recognition into DG and DA, which are formalized as open domain generalization
(ODG) [48, 68], open-set domain adaptation (OSDA) [45, 1, 29, 37, 17, 10, 11]
and universal domain adaptation (UNDA)/open-partial domain adaptation (OPDA)
[12, 23, 64, 42, 43, 27], respectively.

TABLE I: Related setting: $\mathcal{C}_{s}$ and $\mathcal{C}_{t}$ denote label
set of source and target domain (for evaluation), $\mathcal{P}_{s}$ and
$\mathcal{P}_{t}$ denote source and target distribution, transductive means
model can be trained on target data.

Task | $\mathcal{C}_{s}=\mathcal{C}_{t}$ | $\mathcal{P}_{s}=\mathcal{P}_{t}$ | Transductive
---|---|---|---
Open-set Recognition (OSR) | ✗ | ✓ | ✗
Domain Generalization (DG) | ✓ | ✗ | ✗
Open Domain Generalization (ODG) | ✗ | ✗ | ✗
Domain Adaptation (DA) | ✓ | ✗ | ✓
Open-partial Domain adaptation (OPDA) | ✗ | ✗ | ✓

The various settings described above are summarized in Table. I. Usually one
method tailored for a specific setting in Table. I does not work well under a
different setting. Most existing works in Open-set Recognition are
computationally demanding, either requiring the generation of unknown
categories [36] or conducting additional learning [18, 51, 6]. Additionally,
those methods are likely to suffer from performance degradation if test data
are from different distributions. The recent CrossMatch [68] tackles the Open-
set Single Domain Generalization problem. It proposes to use multiple open
class detectors which are put on top of existing single domain generalization
methods, and it achieves good results at the expense of introducing multiple
open-set detectors and auxiliary unknown sample generation. For open-partial
domain adaptation, most works are based on an explicitly designed unknown-
sample rejection module, which typically requires various hyperparameters.
More importantly, those OPDA/UNDA methods all require access to source data
during target adaptation, which is infeasible when having data privacy issues
and when deployed on devices of low computational capacity.

In this paper, we investigate the challenging source-free open-partial domain
adaptation task111Most recent papers refer to open-partial domain adaptation
as universal DA [4, 43, 19], in this paper we adopt open-partial DA to avoid
any potential misunderstanding., where there is no access to source data
during adaptation. Thus, a question arises, how to build a model training from
only known categories aiming to learn to distinguish samples of known and
unknown categories? Since we have no access to unknown class data, we can only
use the known class data to train this classifier. We hypothesize that the
closest (most similar) class to any known class can be an unknown class. Given
the open-endedness of the unknown class, this is a reasonable assumption. This
hypothesis allows us to train the classifier, enforcing the most probable
class to be the ground truth class, and the runner-up class to be the
background class for all source data. This is achieved by introducing an extra
category in the classifier which represents the unknown classes, during
training on samples of known categories (yielding a ($n$ \+ 1)-way classifier
where $n$ is the number of known classes), the classifier is expected to
output the largest score for the ground truth class, and the second-largest
score for the unknown class. By this way, the model can learn to reject
samples of unknown categories by only training with known classes.

Furthermore, our source model with strong capacity to distinguish unknown
categories can be easily adapted to the target domain without access to source
data under the challenging source-free open-partial domain adaptation setting,
where both source and target domains have their private classes. We propose to
simply use a weighted entropy minimization to achieve the adaptation.

We summarize our contributions as below:

  * •

We propose a simple method called OneRing, which excels at recognizing data of
unknown classes after source training, thus it can be easily adapted the
source model to target domain by using weighted entropy minimization under
source-free open-partial domain adaptation setting (SF-OPDA).

  * •

In experiments, we show our method is on par with or outperforms current
state-of-the-art approaches on several benchmarks, which proves the efficacy
and generalization ability of our method. Augmented with a closed-set DA
approach, our source-free method surpasses current open-partial domain
adaptation methods by a significant margin.

##  2 Related Works

Open-set Recognition. Open-set recognition (OSR) aims to recognize samples of
unknown categories which do not exist in the training set. Several recent
methods in OSR do not utilize extra data for training. OpenHybrid [65]
introduces a flow-based density estimation module, and ARPL [6, 5] proposes to
learn a reciprocal point per category, which is intuitively regarded as the
farthest point from the corresponding feature group. More recently [55] shows
that actually OSR performance is enhanced when improving the model performance
on the training set, for example by using improved data augmentation and other
training tricks. In this paper, we propose a simple model training directly
with two cross entropy losses without either auxiliary data or an extra
learning process. Our proposed OneRing classifier shares similarity with
Proser [67], which aims to assign the second-largest logit to the unknown
classes. However, Proser is much more complex compared to ours: it first
trains a good $|C_{s}|$-way closed-set classifier and then augment this
classifier to $|C_{s}|+C$-way, and retrain; Further, it needs to synthesize
novel samples for training the $|C_{s}|+C$-way classifier; And they also need
to calibrate the output of the dummy classifier over the extra validation set
by ensuring 95% of validation data are recognized as known. While in this
paper, we directly train the $|C_{s}|+1$-way classifier with a simple
objective; Another main difference is that they only address open-set
recognition, while in our paper we also consider the domain shift, i.e., the
challenging source-free open-partial domain adaptation.

Domain Adaptation. Early methods to tackle domain adaptation (DA) conduct
feature alignment [32, 50, 54] to eliminate the domain shift. DANN [13], CDAN
[33] and DIRT-T [47] further resort to adversarial training to learn domain
invariable features. Similarly, [22, 35, 44] are based on multiple classifier
discrepancy to achieve alignment between domains. Other methods like SRDC
[52], CST [30] address domain shift from the perspective of either clustering
or improved pseudo labeling. And there are also methods considering category
shift source and target domains. They can be grouped into partial-set DA [2,
3, 28], open-set DA [38, 45, 29, 1] and universal DA [64, 23, 43, 12, 42]
depending on the intersection degree of source and target label space. OVANet
[43] is a universal DA method. It trains extra $n$ binary classifiers with
hard negative classifier sampling to reject unknown samples, OVANet needs to
check the normal classifier head and the corresponding binary classifier for
the final prediction. While in this paper, we simply train a $n+1$-way
classifier with normal cross entropy, and the final prediction is directly
provided by the classifier.

Source-free Domain Adaptation. Recently, several works address source-free
domain adaptation (SFDA), where a source pretrained model is adapted to target
without source data. SHOT [25] proposes to use mutual information maximization
along with pseudo labeling. BAIT [61] adapts MCD [44] to source-free setting.
3C-GAN [24] resorts to fake target-style images generation. HCL [16] conducts
Instance Discrimination [58] over different historical models to cluster
features, with the companion of pseudo labeling. $A^{2}$Net [59] learns extra
classifier specifically for the target domain and introduce a category-wise
matching module for feature clustering. G-SFDA [62] and NRC [60] are all based
on neighborhood clustering through local prediction consistency. AaD [63]
further treats SFDA as a typical unsupervised clustering problem and proposes
to optimize an upper bound of a clustering objective. Beyond closed-set DA, FS
[21] and USFDA [20], which are for source-free open-set and open-partial DA
respectively. However, they both synthesize extra training samples of unknown
categories, which help to detect the open classes. OSHT [11] tackles source-
free open-set DA, which adopts pseudo labeling for adaptation and entropy-
based metric to reject open classes.In this paper, we show that our source
pretrained model can be adapted to the target domain easily by simply
minimizing entropy to achieve source-free open-partial DA.

##  3 Method

###  3.1 Preliminary

Data samples are divided into two domains: the labeled source domain with
$N_{s}$ samples $\mathcal{D}_{s}=\\{(x_{i}^{s},y_{i}^{s})\\}_{i=1}^{N_{s}}$ on
which the model will be first trained, and the unlabeled target domain with
$N_{t}$ samples $\mathcal{D}_{t}=\\{x_{i}^{t}\\}_{i=1}^{N_{t}}$.
$\mathcal{D}_{t}$ is used for evaluation. We denote $\mathcal{C}_{s}$ and
$\mathcal{C}_{t}$ as the label set of the source and target domain, and
$\mathcal{P}_{s}$ and $\mathcal{P}_{t}$ as the distribution of source and
target data, respectively. In this paper, we study the challenging Source-free
open-partial domain adaptation (SF-OPDA) task, where the source pretrained
model has to adapt to the target domain without access to any source data and
both domains have private categories
($\mathcal{C}_{s}\cap\mathcal{C}_{t}\neq\text{ any of
}\\{\emptyset,\mathcal{C}_{s},\mathcal{C}_{t}\\},\mathcal{P}_{s}\neq\mathcal{P}_{t}$).

Figure 1: (Left) Illustration of training OneRing model on source data with
only known categories. (Right) Toy Example, the decision boundaries and
prediction regions (colorized randomly) after training on 3 known classes with
($3+1$)-way classifier. Purple points are from unknown category.

###  3.2 Source Training: One Ring to Find Unknown Categories

The first stage is to train a model on the labeled source domain which has
$|\mathcal{C}_{s}|$ categories. We expect the resulting model to have the
ability to detect unknown categories which do not exist in the source data. To
achieve this, we build a classifier head as a ($|\mathcal{C}_{s}|\bm{+1}$)-way
classifier, where the additional dimension aims to distinguish unknown
categories. Then the following problem arises: how to train a
($|\mathcal{C}_{s}|+1$)-way classifier without any sample from the unknown
category? Note, if only training with the normal cross entropy (CE) loss on
the source data, the model cannot directly give prediction to unknown
categories.

As mentioned in Sec. 1, we hypothesize that any non-ground-truth category
could be regarded as an unknown category. This hypothesis gives us a feasible
solution to train an open-set classifier without actually accessing open
classes. Specifically, we propose to use a simple variant of cross entropy
loss with only samples of known categories to train the
($|\mathcal{C}_{s}|+1$)-way classifier, which has 2 properties: 1) The largest
output logit of the source samples corresponds to the ground truth class and
2) The second-largest output logit of source samples will be the unknown class
(($|\mathcal{C}_{s}|+1$)-th class in classifier). This way, the model is
expected to detect samples of unknown categories even without training on
them. The proposed objective to achieve it is formalized as follows:

$\mathcal{L}_{source}=\mathbb{E}_{x_{i}\sim\mathcal{D}_{s}}[\mathcal{L}_{\textit{ce}}(p(x_{i}),y_{i})+\mathcal{L}_{\textit{ce}}(\hat{p}(x_{i}),\hat{y_{i}})]$
(1)

where $p(x_{i})=g(f(x_{i}))\in\mathbb{R}^{|C_{s}|+1}$ is the output vector of
the ($|\mathcal{C}_{s}|+1$)-way classifier, while
$\hat{p}(x_{i})\in\mathbb{R}^{|C_{s}|}$ is the output vector removing the
dimension corresponding to the ground truth class, and
$\hat{y}_{i}\in\mathbb{R}^{|C_{s}|}$ is a one-hot label with unknown class as
ground truth label. As illustrated in Fig. 1 (right), if we have a sample
$x_{i}$ belonging to the first class, the first CE loss in Eq. 1 is the
typical CE loss on $p(x_{i})$ with ground truth label, $\hat{p}(x_{i})$ is
produced by removing the first dimension and the second CE loss is applied on
$\hat{p}(x_{i})$ with unknown (last) category as label.

We adopt a toy example to illustrate it. As shown in upper part of Fig. 1
(right), we generate isotropic Gaussian blobs with 4 categories, where the
last one is treated as the unknown category (in Purple) and others as known
classes (thus $|\mathcal{C}_{s}|=3$). We first train the
($|\mathcal{C}_{s}|+1$)-way classifier with the normal cross entropy loss on
samples of known categories, and then evaluate it on all classes. The upper
part of Fig. 1 (right) shows that the samples of the hypothetical unknown
category (Purple) are misclassified as there are only 3 prediction regions for
3 known categories. As shown in the lower part of Fig. 1 (right) that there
are 4 prediction regions (3 known + 1 unknown categories), after training on 2
CE losses the classifier can detect samples of unknown category which is
unseen before.

An intuitive understanding of the proposed method is that we can split the
($|\mathcal{C}_{s}|+1$)-way classification into 2 levels: 1) if we check the
prediction $p(x_{i})$ we would say $x_{i}$ has to belong to category $y_{i}$;
2) if we check the prediction $\hat{p}(x_{i})$ we would say that $x_{i}$ is
impossible to belong to all other categories except the potential unknown
categories. Since in Eq. 1 the output score of unknown category (last
dimension) will always rule other non-ground-truth categories, we call the
last dimension of the classifier head as OneRing dimension and our model as
OneRing.

###  3.3 Target Adaptation: One Ring to Bind All Categories without the Source

Our source-pretrained OneRing model is empowered with the ability to
recognition unknown classes in the target domain. We further posit that it can
easily be adapted to target domains where domain shift and unknown categories
exist. The key part is to rectify the wrong predictions due to the domain
shift. We propose to simply use entropy minimization, which is widely used in
DA [47, 33, 25, 42, 43], to achieve adaptation with only a slight but
indispensable modification:

$\mathcal{L}_{target}=\frac{bs}{\hat{n}_{k_{all}}}\mathbb{E}_{\bar{y_{i}}\in\mathcal{C}_{s}}\mathcal{L}_{\textit{ent}}(p(x_{i}))+\frac{bs}{\hat{n}_{u_{all}}}\mathbb{E}_{\bar{y_{i}}\in\mathcal{C}_{u}}\mathcal{L}_{\textit{ent}}(p(x_{i}))$
(2)

which is computed in the mini-batch ($bs$ denotes batch size), and
$\bar{y_{i}}$ is the predicted label, $\hat{n}_{k_{all}}$ is the number of
samples in the whole dataset which are predicted as known category
$\mathcal{C}_{s}$, $\hat{n}_{u_{all}}$ is the number of those predicted as
unknown category $\mathcal{C}_{u}$ also in the whole dataset. Here
$\frac{bs}{\hat{n}_{k_{all}}}=\frac{N_{t}}{\hat{n}_{k_{all}}}\times\frac{bs}{N_{t}}$
(similar for $\frac{bs}{\hat{n}_{u_{all}}}$), where
$N_{t}=\hat{n}_{k_{all}}+\hat{n}_{u_{all}}$ and
$\frac{N_{t}}{\hat{n}_{k_{all}}}$ is the reciprocal of the known/unknown
category ratio (prior information according to the predictions). The reason to
deploy these weights is to balance the two entropy terms, and
$\frac{bs}{N_{t}}$ is a scale factor. 222Instead of using the predictions over
the whole dataset to compute known-unknown ratio, we can also use prediction
of current mini-batch for approximation (thus $N_{t}$ will be replaced by
$bs$, and similar for $\hat{n}_{u_{all}}$ and $\hat{n}_{k_{all}}$), in the
experiment we empirically found these two different estimation manners lead to
almost the same results. With this simple objective, the source model can be
adapted to the target domain under domain and category shift efficiently.

Augmented with Attracting-and-Dispersing. Since our OneRing method can equip
models to efficiently detect unknown classes, it can be used as a baseline to
be combined with methods in closed-set source-free DA. Here we integrate our
method with a simple state-of-the-art SFDA method Attracting-and-Dispersing
(AaD) [63], note AaD can not directly tackle the open-partial domain
adaptation setting. AaD has an objective with only 2 dot product terms:
$\mathcal{L}_{dis}$ for discriminability and $\mathcal{L}_{div}$ for
diversity, more details can be found in AaD paper. The resulting objective is:

$\displaystyle\mathcal{L}_{target+}=\frac{bs}{\hat{n}_{k_{all}}}\mathbb{E}_{\bar{y_{i}}\in\mathcal{C}_{s}}[\mathcal{L}_{\textit{ent}}(p(x_{i}))+\mathcal{L}_{dis}+\mathcal{L}_{div}]$
(3)
$\displaystyle+\frac{bs}{\hat{n}_{u_{all}}}\mathbb{E}_{\bar{y_{i}}\in\mathcal{C}_{u}}[\mathcal{L}_{\textit{ent}}(p(x_{i}))+\mathcal{L}_{dis}]$

where we do not deploy the diversity term for samples predicted as an unknown
class since there is only one single unknown class.

##  4 Experiments

Here we provide quantitative results and analyses related to source-free open-
partial domain adaptation.

###  4.1 Datasets

For SF-OPDA, the model is trained on the source domain first, then adapted to
the target domain without access to any source data. Here both the source and
target domains have their private categories and the target domain has some
unknown categories. We evaluate our method on several benchmarks following the
same setting as previous work in OPDA [64, 42, 43]: 1) Office-31 shares 10
classes with Caltech-256 which will be used as the common categories. Then the
next 10 classes in alphabetical order will be source private, and the
remaining classes will be target private. 2) Office-Home The first 10 classes
in alphabetical order are shared between domains, and the next 5 categories
will be source private, and the remaining classes are target private. 3) VisDA
(VisDA-C 2017) [40] The 6 classes out of 12 classes will be the shared
categories, and source and target domain both have 3 private classes. 4)
DomainNet [39] DomainNet is one of the largest domain adaptation benchmarks
with around 0.6 million images. Following previous works, we will use 3
domains: Painting (P), Real (R), and Sketch (S). We will use the first 150
classes as shared categories, the next 50 classes are source private and the
remaining 145 as target private. The number of source, target and shared
categories is described in the title of each table.

TABLE II: Accuracy (%) on Office-31 and VisDA dataset using ResNet-50. open-
partial domain adaptation where for Office-31: $|\mathcal{C}_{s}|=20$,
$|\mathcal{C}_{t}|=21$, $|\mathcal{C}_{s}\cap\mathcal{C}_{t}|=10$; and for
VisDA: $|\mathcal{C}_{s}|=9$, $|\mathcal{C}_{t}|=9$,
$|\mathcal{C}_{s}\cap\mathcal{C}_{t}|=6$. The second highest H score is
underlined. SF indicates whether source-free.

Office-31 | SF | A2W | D2W | W2D | A2D | D2A | W2A | Avg | VisDA
---|---|---|---|---|---|---|---|---|---
OS | H | OS | H | OS | H | OS | H | OS | H | OS | H | OS | H | H
OSBP [45] | ✗ | 66.1 | 50.2 | 73.6 | 55.5 | 85.6 | 57.2 | 72.9 | 51.1 | 47.4 | 49.8 | 60.5 | 50.2 | 67.7 | 52.3 | 27.3
UAN [64] | ✗ | 85.6 | 58.6 | 94.8 | 70.6 | 98.0 | 71.4 | 86.5 | 59.7 | 85.5 | 60.1 | 85.1 | 60.3 | 89.2 | 63.5 | 30.5
ROS [1] | ✗ | - | 71.3 | - | 94.6 | - | 95.3 | - | 71.4 | - | 81.0 | - | 81.2 | - | 82.1 | -
CMU [12] | ✗ | 86.7 | 67.3 | 96.7 | 79.3 | 98.0 | 80.4 | 89.1 | 68.1 | 88.4 | 71.4 | 88.6 | 72.2 | 91.1 | 73.1 | 34.6
DCC [23] | ✗ | 91.7 | 78.5 | 94.5 | 79.3 | 96.2 | 88.6 | 93.7 | 88.5 | 90.4 | 70.2 | 92.0 | 75.9 | 93.1 | 80.2 | 43.0
DANCE [42] | ✗ | - | 71.5 | - | 91.4 | - | 87.9 | - | 78.6 | - | 79.9 | - | 72.2 | - | 80.3 | 4.4
OVANet [43] | ✗ | - | 79.4 | - | 95.4 | - | 94.3 | - | 85.8 | - | 80.1 | - | 84.0 | - | 86.5 | 53.1
USFDA [20] | ✓ | - | 79.8 | - | 90.6 | - | 81.2 | - | 85.5 | - | 83.2 | - | 88.7 | - | 84.8 | -
OneRing-S |  | 69.0 | 67.9 | 92.5 | 90.6 | 96.5 | 89.4 | 81.9 | 74.9 | 64.8 | 74.8 | 69.9 | 78.8 | 79.1 | 79.4 | 35.2
OneRing | ✓ | 78.8 | 83.8 | 94.7 | 95.2 | 97.5 | 96.0 | 86.6 | 85.7 | 82.0 | 85.8 | 81.0 | 84.7 | 86.8 | 88.5 | 60.7
OneRing+ | ✓ | 85.3 | 85.4 | 94.0 | 94.2 | 97.0 | 93.6 | 88.4 | 86.1 | 88.9 | 90.7 | 87.3 | 84.0 | 90.2 | 89.0 | 66.1

TABLE III: H-score (%) on Office-Home dataset using ResNet-50 as backbone.
open-partial domain adaptation where $|\mathcal{C}_{s}|=15$,
$|\mathcal{C}_{t}|=60$, $|\mathcal{C}_{s}\cap\mathcal{C}_{t}|=10$. The second
highest H score is underlined. SF indicates whether source-free.

| SF | A2C | A2P | A2R | C2A | C2P | C2R | P2A | P2C | P2R | R2A | R2C | R2P | Avg
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
OSBP [45] | ✗ | 39.6 | 45.1 | 46.2 | 45.7 | 45.2 | 46.8 | 45.3 | 40.5 | 45.8 | 45.1 | 41.6 | 46.9 | 44.5
UAN [64] | ✗ | 51.6 | 51.7 | 54.3 | 61.7 | 57.6 | 61.9 | 50.4 | 47.6 | 61.5 | 62.9 | 52.6 | 65.2 | 56.6
CMU [12] | ✗ | 56.0 | 56.9 | 59.1 | 66.9 | 64.2 | 67.8 | 54.7 | 51.0 | 66.3 | 68.2 | 57.8 | 69.7 | 61.6
DCC [23] | ✗ | 58.0 | 54.1 | 58.0 | 74.6 | 70.6 | 77.5 | 64.3 | 73.6 | 74.9 | 81.0 | 75.1 | 80.4 | 70.2
DANCE [42] | ✗ | - | - | - | - | - | - | - | - | - | - | - | - | 49.2
OVANet [43] | ✗ | 62.8 | 75.6 | 78.6 | 70.7 | 68.8 | 75.0 | 71.3 | 58.6 | 80.5 | 76.1 | 64.1 | 78.9 | 71.8
OneRing-S |  | 55.7 | 72.4 | 79.6 | 64.6 | 65.3 | 74.6 | 65.9 | 51.5 | 77.9 | 72.1 | 57.8 | 75.0 | 67.7
OneRing | ✓ | 63.3 | 72.4 | 81.0 | 68.8 | 67.2 | 74.6 | 73.3 | 60.8 | 80.9 | 78.1 | 63.9 | 76.7 | 71.8
OneRing+ | ✓ | 69.5 | 81.4 | 87.9 | 73.2 | 77.9 | 82.4 | 81.5 | 68.6 | 88.1 | 81.1 | 70.5 | 85.7 | 79.0

TABLE IV: H-score (%) on DomainNet using ResNet-50 as backbone. Open-partial
domain adaptation where $|\mathcal{C}_{s}|=200$, $|\mathcal{C}_{t}|=295$,
$|\mathcal{C}_{s}\cap\mathcal{C}_{t}|=150$. The second highest H score is
underlined. SF indicates whether source-free.

Method | SF | P2R | R2P | P2S | S2P | R2S | S2R | Avg
---|---|---|---|---|---|---|---|---
OSBP [45] | ✗ | 33.6 | 33.0 | 30.6 | 30.5 | 30.6 | 33.7 | 32.0
DANCE [42] | ✗ | 21.0 | 47.3 | 37.0 | 27.7 | 46.7 | 21.0 | 33.5
UAN [64] | ✗ | 41.9 | 43.6 | 39.1 | 38.9 | 38.7 | 43.7 | 41.0
CMU [12] | ✗ | 50.8 | 52.2 | 45.1 | 44.8 | 45.6 | 51.0 | 48.3
DCC [23] | ✗ | 56.9 | 50.3 | 43.7 | 44.9 | 43.3 | 56.2 | 49.2
OVANet [43] | ✗ | 56.0 | 51.7 | 47.1 | 47.4 | 44.9 | 57.2 | 50.7
OneRing-S |  | 59.1 | 42.9 | 43.8 | 35.5 | 39.5 | 52.9 | 45.6
OneRing | ✓ | 57.9 | 52.0 | 46.5 | 49.6 | 44.1 | 57.8 | 51.3

###  4.2 Model Details and Evaluation

To ensure fair comparison with previous methods, our method is based on the
original code released by OPDA method OVANet [43] (modified for SF-OPDA).

Training details For SF-OPDA, after finishing source training with Eq. 1, we
will adapt the source pretrained model to target domain without using source
data. Only on the very large DomainNet under SF-OPDA setting we found that our
method had difficulties converging (low accuracy on training labeled source
data due to the existence of the second CE loss). Therefore, we applied a two-
phase training on the source data. In the first phase, we train with the
standard CE loss. Then after convergence, we add the second CE loss
(multiplied by 1 ((P2R)) or 0.2 (others) to ensure little influence on the
accuracy of labeled training data) for a few epochs. For all experiments under
SF-OPDA setting, the OneRing classifier is fixed during target adaptation.
When augmented with AaD [63], we set the hyperparameter $K$ in
$\mathcal{L}_{dis}$ same as AaD, and $\beta$ in $\mathcal{L}_{div}$ as 1.

Figure 2: H value of open-partial domain adaptation on Office-Home. We vary
the number of unknown classes as shown in the x axis. Here ’ours’ denotes
OneRing without being augmented with AaD, OVANet and ROS demand source data.

In the experiment, we will mainly report the harmonic mean, as all previous
methods did, and also the average per-class accuracy over all categories (OS)
on Office-31. Note for SF-OPDA, the model is expected to have high performance
on both known and unknown accuracy, which should result in a high harmonic
mean (H). As pointed out by ROS [1], OS is not a reasonable evaluation metric
and can be quite high even when UNK is 0, since
OS$=\frac{|\mathcal{C}_{s}|}{|\mathcal{C}_{s}|+1}\times$OS∗$+\frac{1}{|\mathcal{C}_{s}|+1}\times$UNK.
In the following tables, we will denote our model trained with only source
data as OneRing-S, model after target adaptation as OneRing, and model
augmented with AaD after target adaptation as One Ring+.

Figure 3: (Left) H value of our source model and entropy based rejection on
A2C of Office-Home. t-SNE visualization of features with either only source
known categories (Middle) or also with 10 source extra unknown categories
(Right) from source model on Artistic of Office-Home, where the cross is the
class prototype. The red denotes known classes while other for unknown class.

###  4.3 Quantitative results

In Table. III-IV, we show the results under open-partial DA setting where SF
column indicates whether source-free. Note that our method does not need
source data during target adaptation. As shown in the tables, our source model
(One Ring-S) already achieves decent H performance. The simple OneRing with
only entropy minimization already outperforms all other methods on all 4
benchmarks, adding AaD [63] into method as shown in Eq. 3 (OneRing+) can
further improve the results significantly, leading to 0.5%, 5.4% and 7.2%
improvement on Office-31, VisDA and Office-Home respectively, and it surpasses
the current state-of-the-art OVANet by 2.5%, 13% and 7.2% on these 3
benchmarks respectively. We also show the detailed results of OS*, UNK and H
in Table. VI.

TABLE V: Ablation study (R2C of Office-Home) on the proposed weight in the weighted entropy minimization. Results of OVANet are from our running based on their official code. R2C | OS* | UNK | OS | H
---|---|---|---|---
OVANet [43] | 55.1 | 70.0 | 56.5 | 61.7
OneRing w/o weight in Eq.2 | 19.2 | 97.8 | 26.3 | 32.1
OneRing | 57.8 | 71.6 | 59.1 | 63.9
OneRing+ | 61.5 | 82.7 | 63.4 | 70.5
TABLE VI: Accuracy (%) on open-partial DA. Results are from one random run.

Office-Home |  |  |  |  |  |  |
---|---|---|---|---|---|---|---
| Ar $\rightarrow$ Cl | Ar $\rightarrow$ Pr | Ar $\rightarrow$ Rw | Cl$\rightarrow$ Ar | Cl $\rightarrow$ Pr | Cl $\rightarrow$ Rw |  |  |
| OS* | UNK | H | OS* | UNK | H | OS* | UNK | H | OS* | UNK | H | OS* | UNK | H | OS* | UNK | H |  |  |
OneRing-S | 42.9 | 79.3 | 55.7 | 75.7 | 69.5 | 72.4 | 91.7 | 70.3 | 79.6 | 52.9 | 82.1 | 64.4 | 60.0 | 71.7 | 65.3 | 75.2 | 74.0 | 74.6 |  |  |
OneRing | 54.1 | 73.9 | 62.5 | 78.5 | 69.8 | 73.9 | 93.3 | 72.5 | 81.6 | 65.9 | 73.0 | 69.3 | 67.5 | 66.1 | 66.8 | 80.0 | 69.0 | 74.1 |  |  |
OneRing+ | 58.5 | 84.4 | 69.1 | 78.3 | 84.8 | 81.4 | 92.6 | 84.4 | 88.3 | 62.7 | 88.2 | 73.3 | 72.1 | 86.3 | 78.6 | 80.4 | 86.0 | 83.1 |  |  |
| Pr $\rightarrow$ Ar | Pr $\rightarrow$ Cl | Pr $\rightarrow$ Rw | Rw$\rightarrow$ Ar | Rw $\rightarrow$ Cl | Rw $\rightarrow$ Pr | Avg
| OS* | UNK | H | OS* | UNK | H | OS* | UNK | H | OS* | UNK | H | OS* | UNK | H | OS* | UNK | H | OS* | UNK | H
OneRing-S | 55.9 | 80.2 | 65.9 | 38.6 | 77.1 | 51.5 | 86.9 | 70.5 | 77.9 | 70.4 | 73.9 | 72.1 | 46.6 | 76.0 | 57.8 | 82.6 | 68.7 | 75.0 | 65.0 | 74.4 | 67.7
OneRing | 73.1 | 73.2 | 73.1 | 52.8 | 70.2 | 60.3 | 91.6 | 73.4 | 81.5 | 77.9 | 78.1 | 78.0 | 57.7 | 70.2 | 63.4 | 88.2 | 70.3 | 78.2 | 73.4 | 71.6 | 71.9
OneRing+ | 77.4 | 86.7 | 82.2 | 58.3 | 82.3 | 68.3 | 92.2 | 84.7 | 88.3 | 76.5 | 86.2 | 81.1 | 61.5 | 82.7 | 70.5 | 86.9 | 85.4 | 86.1 | 74.8 | 85.2 | 79.2

TABLE VII: H-score (%) on Office-Home dataset using ResNet-50 as backbone.
Open-partial domain adaptation where $|\mathcal{C}_{s}|=15$,
$|\mathcal{C}_{t}|=60$, $|\mathcal{C}_{s}\cap\mathcal{C}_{t}|=10$. The second
highest H score is underlined. SF indicates whether source-free. * indicates
using predictions over the whole dataset instead of mini-batch in Eq. 2.

| A2C | A2P | A2R | C2A | C2P | C2R | P2A | P2C | P2R | R2A | R2C | R2P | Avg
---|---|---|---|---|---|---|---|---|---|---|---|---|---
OVANet [43] | 62.8 | 75.6 | 78.6 | 70.7 | 68.8 | 75.0 | 71.3 | 58.6 | 80.5 | 76.1 | 64.1 | 78.9 | 71.8
OneRing-S | 55.7 | 72.4 | 79.6 | 64.6 | 65.3 | 74.6 | 65.9 | 51.5 | 77.9 | 72.1 | 57.8 | 75.0 | 67.7
OneRing | 63.3 | 72.4 | 81.0 | 68.8 | 67.2 | 74.6 | 73.3 | 60.8 | 80.9 | 78.1 | 63.9 | 76.7 | 71.8
OneRing* | 60.9 | 72.1 | 80.9 | 67.7 | 66.0 | 73.7 | 73.1 | 60.4 | 81.4 | 77.7 | 63.4 | 78.2 | 71.3
OneRing+ | 69.5 | 81.4 | 87.9 | 73.2 | 77.9 | 82.4 | 81.5 | 68.6 | 88.1 | 81.1 | 70.5 | 85.7 | 79.0
OneRing*+ | 70.1 | 82.5 | 88.9 | 75.1 | 80.1 | 83.0 | 82.5 | 64.6 | 89.3 | 81.0 | 66.4 | 86.0 | 79.1

###  4.4 Analysis

Compare One Ring with entropy based unknown rejection. We also show the
results with entropy based unknown rejection, where a sample is predicted as
unknown if the entropy (maximal normalized) of the prediction (with normal
classifier head) is higher than a manually set threshold. Fig. 3 (left) shows
the H value of source pretrained model on A2C task of Office-Home under open-
partial DA setting, where the x axis denotes the threshold. Our source model
gets better results without any extra effort.

Visualization of features and class prototypes. In Fig. 3 (Middle), we
visualize the source features and class prototypes (weights of OneRing
classifier) from source model with t-SNE. The prototype of the unknown
category is in the corner with no source features around it. In Fig. 3
(Right), we further visualize 10 extra unknown classes. It shows that those
features of unknown categories will not cluster around any of the known
classes, but they are close to the unknown prototype. This implies that the
OneRing model can efficiently distinguish known and unknown categories.

Importance of weight in entropy minimization. We ablate the weights in entropy
minimization in Eq. 2. If removing weights, the OS*, UNK and H on R2C (Office-
Home) will decrease. In Table. V, we report OS*, UNK, OS and H on VisDA under
open-partial DA, we outperform OVANet on the metrics of both OS and H. It
shows the deployed weights are important and effective to balance the two
terms in Eq. 2.

Robustness to amount of unknown categories. In Fig. 2, we compare our source-
free OneRing (without being augmented with AaD) to ROS [1] and OVANet [43]
under OPDA setting with different amount of unknown categories from target
domain. The results show that our method is robust to the amount of unknown
categories.

Known/unknown ratio estimation through mini-batch or whole dataset. In Eq. 2,
we have two choice to estimate the known/unknown ratio, which will be utilized
to balance the 2 entropy terms. In Table. VII, we show that these 2 different
manners lead to almost the same results. Though there may exist some imbalance
mini-batches which only contain few samples predicted as known or unknown, the
results imply that the known/unknown ratio estimated by the mini-batch is
enough to achieve decent performance. Note the Office-Home here is not a well
balance (amount of samples per category) dataset, and also in the target
domain the unknown categories (50) are much more than known (10).

##  5 Conclusion

In this paper, we first introduce a simple method with the proposed OneRing
classifier head, it possesses strong ability to detect unknown categories from
target data even no matter without or with domain shift after training with
two simple cross entropy losses. Then, we further adapt the model to the
target domain which contains unknown categories, with only weighted entropy
minimization and no access to source data. In the experiment, we show that our
method achieves good performance on source-free open-partial domain
adaptation, which proves the effectiveness of our method.

## Acknowledgments

We acknowledge the support from Huawei Kirin Solution. We further acknowledge
the Spanish projects PID2019-104174GB-I00 and RTI2018-102285-A-I0, and the
CERCA Program of the Generalitat de Catalunya. We thank Qiuyue Yang for
drawing the excellent figure.

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Shiqi Yang joined Learning and Machine Perception (LAMP) team in 2019.10 as a
Ph.D. student advised by Dr. Joost van de Weijer in Computer Vision Center of
Autonomous University of Barcelona, Spain. He received master degree in
Huazhong University of Science and Technology, China, and once worked as a
research associate in Kyoto University, Japan. His research interest focuses
on how to efficiently adapt the pretrained model to real world environment
under domain and category shift, including source-free/continual/open-
set/universal domain adaptation.
---
Yaxing Wang is an associate professor of college of computer science at Nankai
University. His research interests include GANs, image-to-image translation,
domain adaptation and lifelong learning. Prior to joining NKU I was a postdoc
at UAB, CVC, working with Joost van de Weije. I obtained my Ph.D. from
Universitat Autònoma de Barcelona, under the supervision of Joost van de
Weijer. I also experienced amazing internship at IIAI with Fahad shahbaz khan
and Salman Khan.
---
Kai Wang is a postdoctoral researcher at Computer Vision Center, UAB. Before
he obtained the Ph.D. degree from Computer Vision Center, UAB in 2022 under
the supervision of Joost van de Weijer. He received the master degree in image
processing from Jilin University in 2017 and the bachelor degree from Jilin
University in 2014. His main research interests include continual learning,
knowledge distillation, domain adaptation and vision transformers.
---
Shangling Jui is the Chief AI Scientist for Huawei Kirin Chipset Solution. He
is an expert in machine learning, deep learning, and artificial intelligence.
Previously, he was the President of the SAP China Research Center and the SAP
Korea Research Center, responsible for 2400 employees and 150 million USD
research and development annual budget. He was also the CTO of Pactera,
leading innovation projects based on cloud and big data technologies. He is
currently an Expert Reviewer of the Project Committee for China-EU Science and
Technology Co-Operation and a Guest Professor of the Software Institute of
Beijing University. He has published various books and articles about the
Chinese software industry and big data analytics in China, U.K., Australia,
and the USA. He has 27 years of working experience in Germany, the USA, and
China. He received the Magnolia Award from the Municipal Government of
Shanghai, in 2011.
---
Joost van de Weijer received the Ph.D. degree from the University of
Amsterdam, Amsterdam, Netherlands, in 2005. He was a Marie Curie Intra-
European Fellow with INRIA Rhone-Alpes, France, and from 2008 to 2012, he was
a Ramon y Cajal Fellow with the Universitat Autònoma de Barcelona, Barcelona,
Spain, where he is currently a Senior Scientist with the Computer Vision
Center and leader of the Learning and Machine Perception (LAMP) Team. His main
research directions are color in computer vision, continual learning, active
learning, and domain adaptation.
---