# Extending Partial Domain Adaptation Algorithms to the Open-Set Setting
## 2 Related Work and Contributions  
Our algorithm relies on ANNs for DA, as they were successful in classical DA [9; 10] and PDA [2; 3; 8; 11; 12]. Such adversarial schemes for DA typically include a source model, which is comprised of a classifier and a representer network, a target representer network, and a domain discriminator. The source and target representer networks are embeddings of the source and target data, respectively, into some _latent space_. During the adaptation, the target representer and the domain discriminator antagonize each other, while the source representer and classifier are either fixed with pre-trained weights or trained in a supervised fashion.  
At each iteration, the discriminator is presented with data from the source and target domains embedded in latent space and trained to discriminate between the two. The target representer is then trained using reversed discriminator gradients. This process can be shown to minimize the Jensen-Shannon Divergence [13] between the distributions of source and target data in latent space, mitigating the covariate shift problem. This standard scheme is typically augmented by other networks to deal with PDA problems. For example, in [2; 3], a collection of domain discriminators were used to reweigh source instances so as to ignore outlier classes in the source domain. In [8], a second domain discriminator was used to reweigh source instances in a similar fashion. Moreover, in [11], both a domain discriminator and classifier information were used to separate outlier source classes. Finally, in [12], a shared-label classifier was used to re-weight instances, and it was trained using information from the source classifier.  
Contrary to previous works, we reweigh _both_ source and target instances to correct for target outliers. As in [8; 11], we re-weight using a second domain discriminator, but our scheme may be modified to use _any_ heuristic for TRIs. Moreover, we use the obtained weights to identify target outlier instances by setting a threshold on the target instance weights.  
In [6], OSDA was introduced and an algorithm was proposed for tackling OSDA problems based on a constrained integer programming model. A rejection parameter was used to tune open-space risk tolerance. In [14], the authors introduced a method that relied on classifying outlier target instances as \"unknown\", removing the need for a rejection parameter but making classification harder. The model training utilized techniques introduced in [15] for OSR that generated \"unknown\" target instances.  
The main contribution of this work is the adaptation of existing algorithms for PDA to achieve improved performance in the OSDA setting. Our method mitigates the covariateshift problem only for source and target instances that have labels in the common labels' subspace. We further show that our algorithm gives rise to a natural heuristic for identifying target instances that are probably transfer relevant. We perform experiments and empirically validate our approach. In Figure 1, we illustrate the effect of domain adaptation in the presence of source and target outliers.