no code implementations • 27 May 2024 • Sirui Xie, Zhisheng Xiao, Diederik P Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, Ruiqi Gao
We propose EM Distillation (EMD), a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of perceptual quality.
no code implementations • 7 Feb 2024 • Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian Wu
We introduce the Latent Plan Transformer (LPT), a novel model that leverages a latent space to connect a Transformer-based trajectory generator and the final return.
no code implementations • 10 Nov 2023 • Zhide Zhong, Jiakai Cao, Songen Gu, Sirui Xie, Weibo Gao, Liyi Luo, Zike Yan, Hao Zhao, Guyue Zhou
We present ASSIST, an object-wise neural radiance field as a panoptic representation for compositional and realistic simulation.
2 code implementations • 13 Jun 2022 • Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling.
no code implementations • 28 Nov 2021 • Shuwen Qiu, Sirui Xie, Lifeng Fan, Tao Gao, Jungseock Joo, Song-Chun Zhu, Yixin Zhu
Humans communicate with graphical sketches apart from symbolic languages.
no code implementations • 25 Nov 2021 • Chi Zhang, Sirui Xie, Baoxiong Jia, Ying Nian Wu, Song-Chun Zhu, Yixin Zhu
Extensive experiments show that by incorporating an algebraic treatment, the ALANS learner outperforms various pure connectionist models in domains requiring systematic generalization.
2 code implementations • NeurIPS 2021 • Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying and disentangling objects from the background.
no code implementations • 22 Feb 2021 • Sirui Xie, Xiaojian Ma, Peiyu Yu, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances.
no code implementations • 1 Jan 2021 • Chi Zhang, Sirui Xie, Baoxiong Jia, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu
We further show that the algebraic representation learned can be decoded by isomorphism and used to generate an answer.
no code implementations • 12 Nov 2020 • Sirui Xie, Feng Gao, Song-Chun Zhu
Seeing that the proposed generalization problem has not been widely studied yet, we carefully define an evaluation protocol, with which we illustrate the effectiveness of MEIP on two proof-of-concept domains and one challenging task: learning to fold from demonstrations.
1 code implementation • 2 Sep 2020 • Sirui Xie, Shoukang Hu, Xinjiang Wang, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin
To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.
1 code implementation • CVPR 2020 • Shoukang Hu, Sirui Xie, Hehui Zheng, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin
We argue that given a computer vision task for which a NAS method is expected, this definition can reduce the vaguely-defined NAS evaluation to i) accuracy of this task and ii) the total computation consumed to finally obtain a model with satisfying accuracy.
Ranked #16 on Neural Architecture Search on NAS-Bench-201, ImageNet-16-120 (Accuracy (Val) metric)
no code implementations • 30 Nov 2019 • Junning Huang, Sirui Xie, Jiankai Sun, Qiurui Ma, Chunxiao Liu, Jianping Shi, Dahua Lin, Bolei Zhou
In this work, we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning.
1 code implementation • CVPR 2020 • Peiwen Lin, Peng Sun, Guangliang Cheng, Sirui Xie, Xi Li, Jianping Shi
Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with new search space where a lightweight model can be effectively explored through the cell-level diversity and latencyoriented constraint.
2 code implementations • ICLR 2019 • Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin
In experiments on CIFAR-10, SNAS takes less epochs to find a cell architecture with state-of-the-art accuracy than non-differentiable evolution-based and reinforcement-learning-based NAS, which is also transferable to ImageNet.
Ranked #25 on Neural Architecture Search on NAS-Bench-201, CIFAR-10
no code implementations • ICLR 2019 • Sirui Xie, Junning Huang, Lanxin Lei, Chunxiao Liu, Zheng Ma, Wei zhang, Liang Lin
Reinforcement learning agents need exploratory behaviors to escape from local optima.