activity
20202025
most citedDiscovering and Explaining the Representation Bottleneck of DNNs

16 citations · 28 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025★ 1 cited

Automatic Image Colorization with Convolutional Neural Networks and Generative Adversarial Networks

Changyuan Qiu, Hangrui Cao, Qihan Ren +2

Image colorization, the task of adding colors to grayscale images, has been the focus of significant research efforts in computer vision in recent years for its various application…

cs.LG2023★ 2 cited

Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models

Qihan Ren, Jiayang Gao, Wen Shen +1

This study aims to prove the emergence of symbolic concepts (or more precisely, sparse primitive inference patterns) in well-trained deep neural networks (DNNs). Specifically, we p…

cs.LG2023★ 1 cited

Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts

Qihan Ren, Huiqi Deng, Yunuo Chen +2

In this paper, we focus on mean-field variational Bayesian Neural Networks (BNNs) and explore the representation capacity of such BNNs by investigating which types of concepts are…

cs.CV2021★ 7 cited

Interpreting Representation Quality of DNNs for 3D Point Cloud Processing

Wen Shen, Qihan Ren, Dongrui Liu +1

In this paper, we evaluate the quality of knowledge representations encoded in deep neural networks (DNNs) for 3D point cloud processing. We propose a method to disentangle the ove…

cs.LG2021★ 16 cited

Discovering and Explaining the Representation Bottleneck of DNNs

Huiqi Deng, Qihan Ren, Hao Zhang +1

This paper explores the bottleneck of feature representations of deep neural networks (DNNs), from the perspective of the complexity of interactions between input variables encoded…

cs.LG2020★ 1 cited

Rotation-Equivariant Neural Networks for Privacy Protection

Hao Zhang, Yiting Chen, Haotian Ma +5

In order to prevent leaking input information from intermediate-layer features, this paper proposes a method to revise the traditional neural network into the rotation-equivariant…