Chengzhi Mao | DeepAI

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Muhan Zhang
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∙ 05/12/2023

Monitoring and Adapting ML Models on Mobile Devices

ML models are increasingly being pushed to mobile devices, for low-laten...

0 Wei Hao, et al. ∙

∙ 03/22/2023

Test-time Defense against Adversarial Attacks: Detection and Reconstruction of Adversarial Examples via Masked Autoencoder

Existing defense methods against adversarial attacks can be categorized ...

0 Yun-Yun Tsai, et al. ∙

∙ 03/01/2023

Self-Supervised Convolutional Visual Prompts

Machine learning models often fail on out-of-distribution (OOD) samples....

0 Yun-Yun Tsai, et al. ∙

∙ 12/14/2022

Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

Pretrained large-scale vision-language models like CLIP have exhibited s...

0 Chengzhi Mao, et al. ∙

∙ 12/13/2022

Adversarially Robust Video Perception by Seeing Motion

Despite their excellent performance, state-of-the-art computer vision mo...

0 Lingyu Zhang, et al. ∙

∙ 12/12/2022

Doubly Right Object Recognition: A Why Prompt for Visual Rationales

Many visual recognition models are evaluated only on their classificatio...

0 Chengzhi Mao, et al. ∙

∙ 12/12/2022

Robust Perception through Equivariance

Deep networks for computer vision are not reliable when they encounter a...

0 Chengzhi Mao, et al. ∙

∙ 10/07/2022

1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)

Recent years have seen advances on principles and guidance relating to a...

0 Hao Wang, et al. ∙

∙ 06/17/2022

Landscape Learning for Neural Network Inversion

Many machine learning methods operate by inverting a neural network at i...

13 Ruoshi Liu, et al. ∙

∙ 06/17/2022

Shadows Shed Light on 3D Objects

3D reconstruction is a fundamental problem in computer vision, and the t...

0 Ruoshi Liu, et al. ∙

∙ 04/26/2022

Causal Transportability for Visual Recognition

Visual representations underlie object recognition tasks, but they often...

5 Chengzhi Mao, et al. ∙

∙ 04/22/2022

A Tale of Two Models: Constructing Evasive Attacks on Edge Models

Full-precision deep learning models are typically too large or costly to...

0 Wei Hao, et al. ∙

∙ 04/07/2022

Using Multiple Self-Supervised Tasks Improves Model Robustness

Deep networks achieve state-of-the-art performance on computer vision ta...

0 Matthew Lawhon, et al. ∙

∙ 12/14/2021

Real-Time Neural Voice Camouflage

Automatic speech recognition systems have created exciting possibilities...

0 Mia Chiquier, et al. ∙

∙ 11/20/2021

Discrete Representations Strengthen Vision Transformer Robustness

Vision Transformer (ViT) is emerging as the state-of-the-art architectur...

10 Chengzhi Mao, et al. ∙

∙ 03/26/2021

Adversarial Attacks are Reversible with Natural Supervision

We find that images contain intrinsic structure that enables the reversa...

16 Chengzhi Mao, et al. ∙

∙ 12/22/2020

Generative Interventions for Causal Learning

We introduce a framework for learning robust visual representations that...

18 Chengzhi Mao, et al. ∙

∙ 07/14/2020

Multitask Learning Strengthens Adversarial Robustness

Although deep networks achieve strong accuracy on a range of computer vi...

31 Chengzhi Mao, et al. ∙

∙ 04/22/2020

Live Trojan Attacks on Deep Neural Networks

Like all software systems, the execution of deep learning models is dict...

5 Robby Costales, et al. ∙

∙ 10/06/2019

Unrestricted Adversarial Attacks for Semantic Segmentation

Semantic segmentation is one of the most impactful applications of machi...

23 Guangyu Shen, et al. ∙

∙ 09/03/2019

Metric Learning for Adversarial Robustness

Deep networks are well-known to be fragile to adversarial attacks. Using...

15 Chengzhi Mao, et al. ∙

∙ 02/06/2019

Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling

We consider the problem of inferring the values of an arbitrary set of v...

24 Hao Wang, et al. ∙

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