Secondly, the subdomain classifier module based on instance confidence is constructed, which can dynamically divide the target domain into easy and difficult subdomains according to the relative proportion of easy and difficult instances.
To the best of our knowledge, the proposed Nextformer model achieves SOTA results on AISHELL-1(CER 4. 06%) and WenetSpeech(CER 7. 56%/11. 29%).
Ranked #1 on Speech Recognition on AISHELL-1 (CER metric)
Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network.
Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero-Shot Learning (GZSL) problem.
Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem.
Existing VI Re-ID methods mainly focus on extracting homogeneous structural relationships in an image, i. e. the relations between local features, while ignoring the heterogeneous correlation of local features in different modalities.
Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic.
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
The derived generalization bound based on the ITID assumption identifies the significance of hypothesis invariance in guaranteeing generalization performance.
Turing test was originally proposed to examine whether machine's behavior is indistinguishable from a human.
This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change.
SLRM model takes advantage of the nuclear norm regularization on mapping to effectively capture the label correlations.
Categorization axioms have been proposed to axiomatizing clustering results, which offers a hint of bridging the difference between human recognition system and machine learning through an intuitive observation: an object should be assigned to its most similar category.