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Found 23 papers, 9 papers with code
TrailBlazer: Trajectory Control for Diffusion-Based Video Generation
Within recent approaches to text-to-video (T2V) generation, achieving controllability in the synthesized video is often a challenge.
Exact Diffusion Inversion via Bi-directional Integration Approximation
In our work, it is found that applying BDIA to the EDM sampling procedure produces consistently better performance over four pre-trained models.
Lookahead Diffusion Probabilistic Models for Refining Mean Estimation
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.
On Accelerating Diffusion-Based Sampling Process via Improved Integration Approximation
A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE).
Directed Diffusion: Direct Control of Object Placement through Attention Guidance
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.
Estimation of Source and Receiver Positions, Room Geometry and Reflection Coefficients From a Single Room Impulse Response
We propose an algorithm to estimate source and receiver positions, room geometry and reflection coefficients from a single room impulse response simultaneously.
Ultra-Low-Bitrate Speech Coding with Pretrained Transformers
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.
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range
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.
On the relevance of bandwidth extension for speaker identification
In this paper we discuss the relevance of bandwidth extension for speaker identification tasks.
Extending AdamW by Leveraging Its Second Moment and Magnitude
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).
Handling Background Noise in Neural Speech Generation
Recent advances in neural-network based generative modeling of speech has shown great potential for speech coding.
Generative Speech Coding with Predictive Variance Regularization
We introduce predictive-variance regularization to reduce the sensitivity to outliers, resulting in a significant increase in performance.
The HSIC Bottleneck: Deep Learning without Back-Propagation
We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks.
Rapidly Adapting Moment Estimation
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.
GENERALIZED ADAPTIVE MOMENT ESTIMATION
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.
Kernel Density Estimation-Based Markov Models with Hidden State
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.
Wavenet based low rate speech coding
Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used.
Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval
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
Training Deep Neural Networks via Optimization Over Graphs
In this work, we propose to train a deep neural network by distributed optimization over a graph.
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition.
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
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
Domain Generalization for Object Recognition with Multi-task Autoencoders
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.
Domain Adaptive Neural Networks for Object Recognition
We propose a simple neural network model to deal with the domain adaptation problem in object recognition.
