Search Results for author:
Found 11 papers, 4 papers with code
DiverseDialogue: A Methodology for Designing Chatbots with Human-Like Diversity
On average, we observe a 54 percent reduction in the error of average features between human and LLM-generated conversations.
Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation
In this paper, we propose a latent-variable generative model called mixture of dynamical variational autoencoders (MixDVAE) to model the dynamics of a system composed of multiple moving sources.
Unsupervised speech enhancement with deep dynamical generative speech and noise models
This work builds on a previous work on unsupervised speech enhancement using a dynamical variational autoencoder (DVAE) as the clean speech model and non-negative matrix factorization (NMF) as the noise model.
Speech Modeling with a Hierarchical Transformer Dynamical VAE
The dynamical variational autoencoders (DVAEs) are a family of latent-variable deep generative models that extends the VAE to model a sequence of observed data and a corresponding sequence of latent vectors.
DSR: Towards Drone Image Super-Resolution
It would enable drones to fly higher covering larger fields of view, while maintaining a high image quality.
Towards Robust Drone Vision in the Wild
On the other hand, the performance of most existing image super-resolution methods is sensitive to the dataset, specifically, the degradation model between high-resolution and low-resolution images.
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder
In this paper, we present an unsupervised probabilistic model and associated estimation algorithm for multi-object tracking (MOT) based on a dynamical variational autoencoder (DVAE), called DVAE-UMOT.
Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks
Furthermore, as proof of concept, we show that when using our oracle fidelity map we even outperform the fully retrained methods, whether trained on noisy or restored images.
Learning degraded image classification with restoration data fidelity
In this report, we explore the influence of degradation types and levels on four widely-used classification networks, and the use of a restoration network to eliminate the degradation's influence.
Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion
While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising.
ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation
In this case, the recommender is updated continually and periodically with new data that arrives in each update cycle, and the updated model needs to provide recommendations for user activities before the next model update.
