37 citations · 101 across the 9 of their papers we have counts for
9 papers
Noisy Differentiable Architecture Search
Xiangxiang Chu, Bo Zhang
Simplicity is the ultimate sophistication. Differentiable Architecture Search (DARTS) has now become one of the mainstream paradigms of neural architecture search. However, it larg…
Perceptual Image Super-Resolution with Progressive Adversarial Network
Lone Wong, Deli Zhao, Shaohua Wan +1
Single Image Super-Resolution (SISR) aims to improve resolution of small-size low-quality image from a single one. With popularity of consumer electronics in our daily life, this t…
MixPath: A Unified Approach for One-shot Neural Architecture Search
Xiangxiang Chu, Shun Lu, Xudong Li +1
Blending multiple convolutional kernels is proved advantageous in neural architecture design. However, current two-stage neural architecture search methods are mainly limited to si…
Neural Architecture Search on Acoustic Scene Classification
Jixiang Li, Chuming Liang, Bo Zhang +3
Convolutional neural networks are widely adopted in Acoustic Scene Classification (ASC) tasks, but they generally carry a heavy computational burden. In this work, we propose a lig…
Latent Variables on Spheres for Autoencoders in High Dimensions
Deli Zhao, Jiapeng Zhu, Bo Zhang
Variational Auto-Encoder (VAE) has been widely applied as a fundamental generative model in machine learning. For complex samples like imagery objects or scenes, however, VAE suffe…
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search
Xiangxiang Chu, Tianbao Zhou, Bo Zhang +1
Differentiable Architecture Search (DARTS) is now a widely disseminated weight-sharing neural architecture search method. However, it suffers from well-known performance collapse d…
SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Qingyuan Li +2
To discover powerful yet compact models is an important goal of neural architecture search. Previous two-stage one-shot approaches are limited by search space with a fixed depth. I…
Curriculum Learning for Deep Generative Models with Clustering
Deli Zhao, Jiapeng Zhu, Zhenfang Guo +1
Training generative models like Generative Adversarial Network (GAN) is challenging for noisy data. A novel curriculum learning algorithm pertaining to clustering is proposed to ad…
Disentangled Inference for GANs with Latently Invertible Autoencoder
Jiapeng Zhu, Deli Zhao, Bo Zhang +1
Generative Adversarial Networks (GANs) play an increasingly important role in machine learning. However, there is one fundamental issue hindering their practical applications: the…