Search Results for author:
Found 23 papers, 11 papers with code
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module.
Sample-Efficient Neural Architecture Search by Learning Action Space
To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search
To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.
Ranked #18 on
Image Classification
on CIFAR-10
Adversarial Domain Adaptation with Domain Mixup
In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.
Frequency-Modulated Point Cloud Rendering with Easy Editing
We develop an effective point cloud rendering pipeline for novel view synthesis, which enables high fidelity local detail reconstruction, real-time rendering and user-friendly editing.
Boosting Point Clouds Rendering via Radiance Mapping
In this work, we focus on boosting the image quality of point clouds rendering with a compact model design.
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
To this end, we propose to achieve personalized spatial audio by reconstructing 3D human ears with single-view images.
Representation-Agnostic Shape Fields
In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning.
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades.
Computational Physics High Energy Physics - Experiment
Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition
By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature.
Ranked #1 on
Fine-Grained Image Classification
on CarFlag-563
Learning Shape Primitives via Implicit Convexity Regularization
In this work, a novel regularization term named Implicit Convexity Regularization (ICR) imposed on implicit primitive learning is proposed to tackle this problem.
Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning
Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in DSDPSs; and present design, implementation and evaluation of a novel and highly effective DRL-based control framework, which minimizes average end-to-end tuple processing time by jointly learning the system environment via collecting very limited runtime statistics data and making decisions under the guidance of powerful Deep Neural Networks.
Peak-Piloted Deep Network for Facial Expression Recognition
Objective functions for training of deep networks for face-related recognition tasks, such as facial expression recognition (FER), usually consider each sample independently.
Ranked #2 on
Facial Expression Recognition (FER)
on Oulu-CASIA
Crowded Scene Analysis: A Survey
Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis.
MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation
One generator transforms the conditional person image into a coarse image of the target pose globally, and the other is to enhance the detailed quality of the synthetic person image through a local reinforcement network.
Beta Process Multiple Kernel Learning
Specifically, for the kernel representation calculated for each input feature instance, we multiply it element-wise with a latent binary vector named as instance selection variables, which targets at selecting good instances and attenuate the effect of ambiguous ones in the resulting new kernel representation.
Shape Self-Correction for Unsupervised Point Cloud Understanding
Then the destroyed shape and the normal shape are sent into a point cloud network to get representations, which are employed to segment points that belong to distorted parts and further reconstruct them to restore the shape to normal.
Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search
As a result, using manually designed action space to perform NAS often leads to sample-inefficient explorations of architectures and thus can be sub-optimal.
Gradient Correction beyond Gradient Descent
GCGD consists of two plug-in modules: 1) inspired by the idea of gradient prediction, we propose a \textbf{GC-W} module for weight gradient correction; 2) based on Neural ODE, we propose a \textbf{GC-ODE} module for hidden states gradient correction.
Contrastive Regression for Domain Adaptation on Gaze Estimation
Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance.
Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification
Deep neural networks (DNNs) that tackle the time series classification (TSC) task have provided a promising framework in signal processing.
PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction
However, simply concatenating SER and RE serially can lead to severe error propagation, and it fails to handle cases like multi-line entities in real scenarios.
Spatial-Aware Latent Initialization for Controllable Image Generation
Recently, text-to-image diffusion models have demonstrated impressive ability to generate high-quality images conditioned on the textual input.
