no code implementations • 13 Sep 2023 • Jianqiao Wangni
We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF).
1 code implementation • 4 Sep 2023 • Jiayan Teng, Wendi Zheng, Ming Ding, Wenyi Hong, Jianqiao Wangni, Zhuoyi Yang, Jie Tang
Diffusion models achieved great success in image synthesis, but still face challenges in high-resolution generation.
Ranked #1 on Image Generation on CelebA-HQ 256x256
no code implementations • 29 Oct 2022 • Jianqiao Wangni
We propose a robust variant of boosting forest to the various adversarial defense methods, and apply it to enhance the robustness of the deep neural network.
no code implementations • 11 Aug 2022 • Ke Xu, Jianqiao Wangni, Yifan Zhang, Deheng Ye, Jiaxiang Wu, Peilin Zhao
Therefore, a threshold quantization strategy with a relatively small error is adopted in QCMD adagrad and QRDA adagrad to improve the signal-to-noise ratio and preserve the sparsity of the model.
1 code implementation • CVPR 2019 • Shaohui Liu, Xiao Zhang, Jianqiao Wangni, Jianbo Shi
We introduce the concept of normalized diversity which force the model to preserve the normalized pairwise distance between the sparse samples from a latent parametric distribution and their corresponding high-dimensional outputs.
Conditional Image Generation Generative Adversarial Network +2
no code implementations • 24 Jan 2019 • Jianqiao Wangni, Ke Li, Jianbo Shi, Jitendra Malik
Recently, researchers proposed various low-precision gradient compression, for efficient communication in large-scale distributed optimization.
no code implementations • 29 Dec 2018 • Jianqiao Wangni, Dahua Lin, Ji Liu, Kostas Daniilidis, Jianbo Shi
For recovering 3D object poses from 2D images, a prevalent method is to pre-train an over-complete dictionary $\mathcal D=\{B_i\}_i^D$ of 3D basis poses.
no code implementations • 7 Dec 2017 • Jianqiao Wangni, Jingwei Zhuo, Jun Zhu
Since the algorithm consumes a major computation cost in the testing phase, we propose a novel teacher-learner framework of learning computation-efficient kernel embeddings from specific data.
no code implementations • 8 Nov 2017 • Jianqiao Wangni, Dahua Lin
To the best of our knowledge, this is the first convergence analysis of the 3D recovery problem.
no code implementations • NeurIPS 2018 • Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang
Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures.
no code implementations • 26 Apr 2017 • Jianqiao Wangni
The $L_1$-regularized models are widely used for sparse regression or classification tasks.