52 citations · 310 across the 37 of their papers we have counts for
8 papers · 1 filter
Dimensionality-Varying Diffusion Process
Han Zhang, Ruili Feng, Zhantao Yang +7
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension. We argue that, con…
Neural Dependencies Emerging from Learning Massive Categories
Ruili Feng, Kecheng Zheng, Kai Zhu +7
This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category c…
Rank Diminishing in Deep Neural Networks
Ruili Feng, Kecheng Zheng, Yukun Huang +3
The rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. In pa…
Principled Knowledge Extrapolation with GANs
Ruili Feng, Jie Xiao, Kecheng Zheng +4
Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous wor…
On Noise Injection in Generative Adversarial Networks
Ruili Feng, Deli Zhao, Zhengjun Zha
Noise injection has been proved to be one of the key technique advances in generating high-fidelity images. Despite its successful usage in GANs, the mechanism of its validity is s…
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…
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…