no code implementations • 12 Mar 2024 • Dong Shu, Tianle Chen, Mingyu Jin, Yiting Zhang, Chong Zhang, Mengnan Du, Yongfeng Zhang
The task of predicting multiple links within knowledge graphs (KGs) stands as a challenge in the field of knowledge graph analysis, a challenge increasingly resolvable due to advancements in natural language processing (NLP) and KG embedding techniques.
1 code implementation • 23 Oct 2023 • Tai-Yu Pan, Chenyang Ma, Tianle Chen, Cheng Perng Phoo, Katie Z Luo, Yurong You, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.
1 code implementation • 9 May 2023 • Tianle Chen, Zheda Mai, Ruiwen Li, Wei-Lun Chao
Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious pixel-level annotation by using only image-level annotation.
no code implementations • 22 Feb 2022 • Cheng Zhang, Tai-Yu Pan, Tianle Chen, Jike Zhong, WenJin Fu, Wei-Lun Chao
One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects.
no code implementations • 1 Jan 2021 • Mahsa Baktashmotlagh, Tianle Chen, Mathieu Salzmann
In this setting, existing techniques focus on the challenging task of isolating the unknown target samples, so as to avoid the negative transfer resulting from aligning the source feature distributions with the broader target one that encompasses the additional unknown classes.
no code implementations • 3 Feb 2020 • Shuo Wang, Tianle Chen, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen
In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner.
no code implementations • 18 Jan 2020 • Shuo Wang, Tianle Chen, Shangyu Chen, Carsten Rudolph, Surya Nepal, Marthie Grobler
Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency.
no code implementations • 10 Jan 2020 • Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen
In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}.
no code implementations • 6 Jan 2020 • Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen
We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.
1 code implementation • 29 Dec 2018 • Tianle Chen, Brian Keng, Javier Moreno
However, buyer purchase patterns are extremely diverse and sparse on a per-product level due to population heterogeneity as well as dependence in purchase patterns across product categories.