336 citations · 1.7k across the 24 of their papers we have counts for
7 papers · 1 filter
Elucidating the Exposure Bias in Diffusion Models
Mang Ning, Mingxiao Li, Jianlin Su +2
Diffusion models have demonstrated impressive generative capabilities, but their \textit{exposure bias} problem, described as the input mismatch between training and sampling, lack…
ZLPR: A Novel Loss for Multi-label Classification
Jianlin Su, Mingren Zhu, Ahmed Murtadha +3
In the era of deep learning, loss functions determine the range of tasks available to models and algorithms. To support the application of deep learning in multi-label classificati…
Sparse-softmax: A Simpler and Faster Alternative Softmax Transformation
Shaoshi Sun, Zhenyuan Zhang, BoCheng Huang +4
The softmax function is widely used in artificial neural networks for the multiclass classification problems, where the softmax transformation enforces the output to be positive an…
GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint
Jianlin Su
We know SGAN may have a risk of gradient vanishing. A significant improvement is WGAN, with the help of 1-Lipschitz constraint on discriminator to prevent from gradient vanishing.…
Training Generative Adversarial Networks Via Turing Test
Jianlin Su
In this article, we introduce a new mode for training Generative Adversarial Networks (GANs). Rather than minimizing the distance of evidence distribution and the ge…
f-VAEs: Improve VAEs with Conditional Flows
Jianlin Su, Guang Wu
In this paper, we integrate VAEs and flow-based generative models successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid images, solved the blurred-image probl…
Variational Inference: A Unified Framework of Generative Models and Some Revelations
Jianlin Su
We reinterpreting the variational inference in a new perspective. Via this way, we can easily prove that EM algorithm, VAE, GAN, AAE, ALI(BiGAN) are all special cases of variationa…