Monocular 3D object detection (Mono3D) has achieved unprecedented success with the advent of deep learning techniques and emerging large-scale autonomous driving datasets. However, drastic performance degradation remains an unwell-studied challenge for practical cross-domain deployment as lack labels on target domain. In this paper, we first comprehensively investigate significant underlying fa...

We propose to extend the marginalized denoising autoencoder (MDA) framework with a domain regularization whose aim is to denoise both the source and target data in such a way that the features become domain invariant and the adaptation gets easier. The domain regularization, based either on the maximum mean discrepancy (MMD) measure or on the domain prediction, aims to reduce the distance betwe...

The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains. Alleviating the domain shift problem, especially in the challenging setting where no labeled data are available for the target domain, is par...

Unsupervised domain adaptation (UDA) deals with the task that labeled training and unlabeled test data collected from source and target domains, respectively. In this paper, we particularly address the practical and challenging scenario of imbalanced cross-domain data. That is, we do not assume the label numbers across domains to be the same, and we also allow the data in each domain to be coll...

Topic adaptation in automatic speech recognition (ASR) refers to the adaptation of language model and vocabulary for improved recognition of in-domain speech data. In this work we implement unsupervised topic adaptation for morph-based ASR, to improve recognition of foreign entity names. Based on first-pass ASR hypothesis similar texts are selected from a collection of articles, which are used ...

Modeling of foreign entity names is an important unsolved problem in morpheme-based modeling that is common in morphologically rich languages. In this paper we present an unsupervised vocabulary adaptation method for morph-based speech recognition. Foreign word candidates are detected automatically from in-domain text through the use of letter n-gram perplexity. Over-segmented foreign entity na...

This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain. Previous domain adaptation methods generally assume the identical label spaces, such that reducing the distribution divergence leads to feasible knowledge transfer. Ho...