no code implementations • 25 May 2024 • Jieren Deng, Hanbin Hong, Aaron Palmer, Xin Zhou, Jinbo Bi, Kaleel Mahmood, Yuan Hong, Derek Aguiar
Randomized smoothing has become a leading method for achieving certified robustness in deep classifiers against l_{p}-norm adversarial perturbations.
no code implementations • 4 Mar 2024 • Jieren Deng, Haojian Zhang, Kun Ding, Jianhua Hu, Xingxuan Zhang, Yunkuan Wang
This paper presents Incremental Vision-Language Object Detection (IVLOD), a novel learning task designed to incrementally adapt pre-trained Vision-Language Object Detection Models (VLODMs) to various specialized domains, while simultaneously preserving their zero-shot generalization capabilities for the generalized domain.
no code implementations • 23 Feb 2024 • Jieren Deng, Aaron Palmer, Rigel Mahmood, Ethan Rathbun, Jinbo Bi, Kaleel Mahmood, Derek Aguiar
Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e. g., self-driving cars or medical imaging.
no code implementations • 5 Dec 2023 • Haoran Tang, Xin Zhou, Jieren Deng, Zhihong Pan, Hao Tian, Pratik Chaudhari
Newly developed diffusion-based techniques have showcased phenomenal abilities in producing a wide range of high-quality images, sparking considerable interest in various applications.
no code implementations • 14 Jun 2023 • Jieren Deng, Xin Zhou, Hao Tian, Zhihong Pan, Derek Aguiar
The bokeh effect is an artistic technique that blurs out-of-focus areas in a photograph and has gained interest due to recent developments in text-to-image synthesis and the ubiquity of smart-phone cameras and photo-sharing apps.
no code implementations • 9 Mar 2023 • Jieren Deng, Xin Zhou, Hao Tian, Zhihong Pan, Derek Aguiar
Distilling the structured information captured in feature maps has contributed to improved results for object detection tasks, but requires careful selection of baseline architectures and substantial pre-training.
no code implementations • 7 Apr 2022 • Jieren Deng, Jianhua Hu, Haojian Zhang, Yunkuan Wang
Class incremental learning(CIL) has attracted much attention, but most existing related works focus on fine-tuning the entire representation model, which inevitably results in much catastrophic forgetting.
no code implementations • EMNLP 2021 • Jieren Deng, Chenghong Wang, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks.
no code implementations • 28 Nov 2021 • Sahidul Islam, Jieren Deng, Shanglin Zhou, Chen Pan, Caiwen Ding, Mimi Xie
Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enabling sustainable smart applications.
1 code implementation • Findings (EMNLP) 2021 • Jieren Deng, Yijue Wang, Ji Li, Chao Shang, Cao Qin, Hang Liu, Sanguthevar Rajasekaran, Caiwen Ding
In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to reconstruct the local training data.
Federated Learning Cryptography and Security
no code implementations • 14 Sep 2020 • Yijue Wang, Jieren Deng, Dan Guo, Chenghong Wang, Xianrui Meng, Hang Liu, Caiwen Ding, Sanguthevar Rajasekaran
Distributed learning such as federated learning or collaborative learning enables model training on decentralized data from users and only collects local gradients, where data is processed close to its sources for data privacy.
no code implementations • 3 Sep 2020 • Sheng Lin, Chenghong Wang, Hongjia Li, Jieren Deng, Yanzhi Wang, Caiwen Ding
Nowadays, Deep Neural Networks are widely applied to various domains.