[PDF] Top 20 Self-Adaptation for Unsupervised Domain Adaptation

... 12 domain-pairs Self- Adapt is obtaining the best performance, the dif- ference of performance between DANN and Self- Adapt is not statistically ...outperforming Self-Adapt in 8 domain- ... See full document

... In contrast to SCL that learns a linear trans- foramtion between pivot and non-pivot features, the next line of work aimed to learn representa- tions with non-linear models, without making the distinction between pivot ... See full document

... main adaptation in computer vision and machine learning ...the domain alignment approaches can only reduce, but not remove the domain shift, target domain samples distributed near the edge of ... See full document

... Importance weighting is a generalization of various statistical bias correction tech- niques. While our labeled data in NLP is heavily biased, importance weighting has seen only few applications in NLP, most of them ... See full document

... given domain. This solution, however, has two evident drawbacks: i) domain-specific models can only be invoked by input sentences presented along with domain in- formation, so that each instance is ... See full document

... for unsupervised domain adap- tation use shared autoencoders to create cross- domain representations (Glorot et ...create domain- invariant representations through adversarial train- ...(e.g., ... See full document

... Resources for implicit discourse relations are scarce compared to the explicit ones, since they are harder to annotate (Miltsakaki et al., 2004). For example, among corpora annotated with dis- course relations such as ... See full document

... model adaptation approach to deal with data variability for speaker diarization in a broadcast ...the domain mismatch by means of supervised model adaptation ...an unsupervised ... See full document

... an unsupervised do- main adaptation for Word Sense Disambigua- tion (WSD) using Stacked Denoising Autoen- coder ...an unsupervised learn- ing method of obtaining the abstract feature set of input ... See full document

... Since smartwatches are a relatively new technology, only few previous works exist on the topic. Of particular relevance is the work in [112], which specifically addresses the use of smartwatches as gesture-based input ... See full document

... of domain adaptation can be di- vided into two groups from the viewpoint of whether labeled data is to be used in the target do- ...while unsupervised learning does not use labeled ...to ... See full document

... Domain adaptation aims to use labeled data from a source domain to help build a system for a target domain, possibly with a small amount of labeled data from the target ...target domain ... See full document

... Unsupervised domain adaptation often re- lies on transforming the instance represen- tation. However, most such approaches are designed for bag-of-words models, and ignore the structured features ... See full document

... our self-training approaches, thus we first assess the performance of the confidence- based methods on the development set for the se- lection of high-quality dependency ... See full document

... The performances of all methods on the Office-home dataset are reported in Table 2. The proposed model Our (H+L) achieves consistent improvements over the compari- son methods. For Office-home dataset, the training ... See full document

... The augmented feature space that has been extracted as described in the previous subsection, contains all features of the source domain , and new features from the target domain, each of which is ... See full document

... existing domain adaptation algorithms on the ...three unsupervised domain adaptation methods from the machine learning literature, and find that multiple methods obtain similarly modest ... See full document

... for unsupervised domain adaptation, but existing approaches have two major weak- ...Second, unsupervised domain adaptation is typically treated as a task of moving from a single ... See full document

... these unsupervised domain adaptation approaches on part-of-speech tagging for historical English (the PPCMBE and the PPCEME), in two settings: (1) temporal adaptation within each indi- vidual ... See full document

... This work evaluated on the widely-used Ama- zon sentiment dataset from Blitzer et al. (2007). However, we believe that future work on do- main adaptation should phase out the use of this dataset. 3 The test set ... See full document