no code implementations • 19 Aug 2023 • Tiehang Duan, Zhenyi Wang, Gianfranco Doretto, Fang Li, Cui Tao, Donald Adjeroh
In this work, we propose a principled approach to perform dynamic evolution on the data for improvement of decoding robustness.
no code implementations • CVPR 2023 • Zhenyi Wang, Li Shen, Donglin Zhan, Qiuling Suo, Yanjun Zhu, Tiehang Duan, Mingchen Gao
To make them trustworthy and robust to corruptions deployed in safety-critical scenarios, we propose a meta-learning framework of self-adaptive data augmentation to tackle the corruption robustness in CL.
1 code implementation • 3 Sep 2022 • Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Donglin Zhan, Tiehang Duan, Mingchen Gao
Two key challenges arise in this more realistic setting: (i) how to use unlabeled data in the presence of a large amount of unlabeled out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting on previously learned task distributions due to the task distribution shift.
1 code implementation • 15 Jul 2022 • Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao
To address these problems, for the first time, we propose a principled memory evolution framework to dynamically evolve the memory data distribution by making the memory buffer gradually harder to be memorized with distributionally robust optimization (DRO).
1 code implementation • CVPR 2022 • Zhenyi Wang, Li Shen, Tiehang Duan, Donglin Zhan, Le Fang, Mingchen Gao
We propose a domain shift detection technique to capture latent domain change and equip the meta optimizer with it to work in this setting.
1 code implementation • 28 Dec 2021 • Tiehang Duan, Zhenyi Wang, Sheng Liu, Sargur N. Srihari, Hui Yang
In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process.
1 code implementation • ICCV 2021 • Zhenyi Wang, Tiehang Duan, Le Fang, Qiuling Suo, Mingchen Gao
In this paper, we explore a more practical and challenging setting where task distribution changes over time with domain shift.
no code implementations • 1 Jan 2021 • Zhenyi Wang, Tiehang Duan, Donglin Zhan, Changyou Chen
However, a natural generalization to the sequential domain setting to avoid catastrophe forgetting has not been well investigated.
1 code implementation • 7 Sep 2020 • Mohammad Abuzar Shaikh, Tiehang Duan, Mihir Chauhan, Sargur Srihari
The task of writer verification is to provide a likelihood score for whether the queried and known handwritten image samples belong to the same writer or not.
Ranked #1 on Handwriting Verification on AND Dataset
2 code implementations • 13 Mar 2020 • Tiehang Duan, Mihir Chauhan, Mohammad Abuzar Shaikh, Jun Chu, Sargur Srihari
The pattern of Electroencephalogram (EEG) signal differs significantly across different subjects, and poses challenge for EEG classifiers in terms of 1) effectively adapting a learned classifier onto a new subject, 2) retaining knowledge of known subjects after the adaptation.
1 code implementation • 25 Dec 2018 • Tiehang Duan, José P. Pinto, Xiaohui Xie
Motivation: With the development of droplet based systems, massive single cell transcriptome data has become available, which enables analysis of cellular and molecular processes at single cell resolution and is instrumental to understanding many biological processes.
no code implementations • 29 Nov 2018 • Tiehang Duan, Qi Lou, Sargur N. Srihari, Xiaohui Xie
In this paper, the documents are modeled as the joint of bags of words, sequential features and word embeddings.