no code implementations • 31 Dec 2023 • Wan-Duo Kurt Ma, J. P. Lewis, W. Bastiaan Kleijn
Within recent approaches to text-to-video (T2V) generation, achieving controllability in the synthesized video is often a challenge.
1 code implementation • 10 Jul 2023 • Guoqiang Zhang, J. P. Lewis, W. Bastiaan Kleijn
In our work, it is found that applying BDIA to the EDM sampling procedure produces consistently better performance over four pre-trained models.
1 code implementation • CVPR 2023 • Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
We propose lookahead diffusion probabilistic models (LA-DPMs) to exploit the correlation in the outputs of the deep neural networks (DNNs) over subsequent timesteps in diffusion probabilistic models (DPMs) to refine the mean estimation of the conditional Gaussian distributions in the backward process.
no code implementations • 22 Apr 2023 • Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE).
no code implementations • 25 Feb 2023 • Wan-Duo Kurt Ma, J. P. Lewis, Avisek Lahiri, Thomas Leung, W. Bastiaan Kleijn
Text-guided diffusion models such as DALLE-2, Imagen, eDiff-I, and Stable Diffusion are able to generate an effectively endless variety of images given only a short text prompt describing the desired image content.
no code implementations • 22 Jan 2023 • Wangyang Yu, W. Bastiaan Kleijn
We propose an algorithm to estimate source and receiver positions, room geometry and reflection coefficients from a single room impulse response simultaneously.
no code implementations • 5 Jul 2022 • Ali Siahkoohi, Michael Chinen, Tom Denton, W. Bastiaan Kleijn, Jan Skoglund
Our numerical experiments show that supplementing the convolutional encoder of a neural speech codec with Transformer speech embeddings yields a speech codec with a bitrate of $600\,\mathrm{bps}$ that outperforms the original neural speech codec in synthesized speech quality when trained at the same bitrate.
1 code implementation • 24 Mar 2022 • Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
Firstly, we show that the particular placement of the parameter epsilon within the update expressions of AdaBelief reduces the range of the adaptive stepsizes, making AdaBelief closer to SGD with momentum.
no code implementations • 24 Feb 2022 • Marcos Faundez-Zanuy, Mattias Nilsson, W. Bastiaan Kleijn
In this paper we discuss the relevance of bandwidth extension for speaker identification tasks.
no code implementations • 9 Dec 2021 • Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
Aida is designed to compute the qth power of the magnitude in the form of |m_{t+1}|^q/(r_{t+1}+epsilon)^(q/p) (or |m_{t+1}|^q/((r_{t+1})^(q/p)+epsilon)), which reduces to that of AdamW when (p, q)=(2, 1).
1 code implementation • 23 Feb 2021 • Tom Denton, Alejandro Luebs, Felicia S. C. Lim, Andrew Storus, Hengchin Yeh, W. Bastiaan Kleijn, Jan Skoglund
Recent advances in neural-network based generative modeling of speech has shown great potential for speech coding.
1 code implementation • 18 Feb 2021 • W. Bastiaan Kleijn, Andrew Storus, Michael Chinen, Tom Denton, Felicia S. C. Lim, Alejandro Luebs, Jan Skoglund, Hengchin Yeh
We introduce predictive-variance regularization to reduce the sensitivity to outliers, resulting in a significant increase in performance.
3 code implementations • 5 Aug 2019 • Wan-Duo Kurt Ma, J. P. Lewis, W. Bastiaan Kleijn
We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks.
no code implementations • 24 Feb 2019 • Guo-Qiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the individual learning rates.
no code implementations • 27 Sep 2018 • Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
Empirical studies for training four convolutional neural networks over MNIST and CIFAR10 show that under proper parameter selection, Game produces promising validation performance as compared to AMSGrad and PAdam.
no code implementations • 30 Jul 2018 • Gustav Eje Henter, Arne Leijon, W. Bastiaan Kleijn
We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes.
1 code implementation • 1 Dec 2017 • W. Bastiaan Kleijn, Felicia S. C. Lim, Alejandro Luebs, Jan Skoglund, Florian Stimberg, Quan Wang, Thomas C. Walters
Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used.
no code implementations • 28 May 2017 • Peng Xu, Qiyue Yin, Yongye Huang, Yi-Zhe Song, Zhanyu Ma, Liang Wang, Tao Xiang, W. Bastiaan Kleijn, Jun Guo
Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo.
Ranked #5 on Sketch-Based Image Retrieval on Chairs
no code implementations • 11 Feb 2017 • Guo-Qiang Zhang, W. Bastiaan Kleijn
In this work, we propose to train a deep neural network by distributed optimization over a graph.
2 code implementations • 12 Jul 2016 • Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, Wen Li
In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition.
no code implementations • 15 Oct 2015 • Muhammad Ghifary, David Balduzzi, W. Bastiaan Kleijn, Mengjie Zhang
We propose Scatter Component Analyis (SCA), a fast representation learning algorithm that can be applied to both domain adaptation and domain generalization.
Ranked #7 on Domain Adaptation on Office-Caltech
3 code implementations • ICCV 2015 • Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, David Balduzzi
The problem of domain generalization is to take knowledge acquired from a number of related domains where training data is available, and to then successfully apply it to previously unseen domains.
no code implementations • 21 Sep 2014 • Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang
We propose a simple neural network model to deal with the domain adaptation problem in object recognition.