Convolutional Neural Networks (CNNs) have brought revolutionary advances to
many research areas due to their capacity of learning from raw data. However,
when those methods are applied to non-controllable environments, many different
factors can degrade the model's expected performance, such as unlabeled
datasets with different levels of domain shift and category shift.
Particularly, when both issues occur at the same time, we tackle this
challenging setup as Open Set Domain Adaptation (OSDA) problem. In general,
existing OSDA approaches focus their efforts only on aligning known classes or,
if they already extract possible negative instances, use them as a new category
learned with supervision during the course of training. We propose a novel way
to improve OSDA approaches by extracting a high-confidence set of unknown
instances and using it as a hard constraint to tighten the classification
boundaries of OSDA methods. Especially, we adopt a new loss constraint
evaluated in three different means, (1) directly with the pristine negative
instances; (2) with randomly transformed negatives using data augmentation
techniques; and (3) with synthetically generated negatives containing
adversarial features. We assessed all approaches in an extensive set of
experiments based on OVANet, where we could observe consistent improvements for
two public benchmarks, the Office-31 and Office-Home datasets, yielding
absolute gains of up to 1.3% for both Accuracy and H-Score on Office-31 and
5.8% for Accuracy and 4.7% for H-Score on Office-Home.

通过提取未知实例的高置信度集合，并将其用作限制条件来收紧开放集域自适应方法的分类边界，改进了开放集域自适应方法。在包含 Office-31 和 Office-Home 数据集的广泛实验中，对 OVANet 进行评估，观察到连续改进的结果，Office-31 的准确率和 H-Score 绝对增益高达 1.3％，Office-Home 的准确率增益为 5.8％，H-Score 增益为 4.7％。

通过未知利用在开放集领域适应中收紧分类边界

Tightening Classification Boundaries in Open Set Domain Adaptation  through Unknown Exploitation

Minimizing error in prevalence estimates and diagnostic classifiers remains a
challenging task in serology. In theory, these problems can be reduced to
modeling class-conditional probability densities (PDFs) of measurement
outcomes, which control all downstream analyses. However, this task quickly
succumbs to the curse of dimensionality, even for assay outputs with only a few
dimensions (e.g. target antigens). To address this problem, we propose a
technique that uses empirical training data to classify samples and estimate
prevalence in arbitrary dimension without direct access to the conditional
PDFs. We motivate this method via a lemma that relates relative conditional
probabilities to minimum-error classification boundaries. This leads us to
formulate an optimization problem that: (i) embeds the data in a parameterized,
curved space; (ii) classifies samples based on their position relative to a
coordinate axis; and (iii) subsequently optimizes the space by minimizing the
empirical classification error of pure training data, for which the classes are
known. Interestingly, the solution to this problem requires use of a
homotopy-type method to stabilize the optimization. We then extend the analysis
to the case of impure training data, for which the classes are unknown. We find
that two impure datasets suffice for both prevalence estimation and
classification, provided they satisfy a linear independence property. Lastly,
we discuss how our analysis unifies discriminative and generative learning
techniques in a common framework based on ideas from set and measure theory.
Throughout, we validate our methods in the context of synthetic data and a
research-use SARS-CoV-2 enzyme-linked immunosorbent (ELISA) assay.

在血清学中，我们提出了一种用于分类样本和估计未知维度中流行率的技术，通过将数据嵌入参数化的曲线空间，并通过最小化纯训练数据的经验分类误差来优化空间。