A Stacked Multi-Granularity Convolution Denoising Auto-Encoder
نویسندگان
چکیده
منابع مشابه
Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation
Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. However, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Examples of these approaches include an approach that uses a polarity dictionary that cannot handle unknown or newly invented words and ano...
متن کاملSpeech enhancement with weighted denoising auto-encoder
A novel speech enhancement method with Weighted Denoising Auto-encoder (WDA) is proposed in this paper. A weighted reconstruction loss function is introduced to the conventional Denoising Auto-encoder (DA), and makes it suitable for the task of speech enhancement. First, the proposed WDA is used to model the relationship between the noisy and clean power spectrums of speech signal. Then, the es...
متن کاملCascading Denoising Auto-Encoder as a Deep Directed Generative Model
Recent work (Bengio et al., 2013) has shown how Denoising Auto-Encoders(DAE) become generative models as a density estimator. However, in practice, the framework suffers from a mixing problem in the MCMC sampling process and no direct method to estimate the test loglikelihood. We consider a directed model with an stochastic identity mapping (simple corruption process) as an inference model and ...
متن کاملDeep Denoising Auto-encoder for Statistical Speech Synthesis
This paper proposes a deep denoising auto-encoder technique to extract better acoustic features for speech synthesis. The technique allows us to automatically extract low-dimensional features from high dimensional spectral features in a non-linear, data-driven, unsupervised way. We compared the new stochastic feature extractor with conventional mel-cepstral analysis in analysis-by-synthesis and...
متن کاملTrust-aware Collaborative Denoising Auto-Encoder for Top-N Recommendation
Both feedback of ratings and trust relationships can be used to reveal user preference to improve recommendation performance, especially for cold users. However, the high-order correlations between tow kind of data are always ignored by existing works. Towards this problem, we propose a Correlative Denoising Autoencoder (CoDAE) model to learn correlations from both rating and trust data for Top...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2918409