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09/04/2023
Code Representation Pre-training with Complements from Program Executions
Large language models (LLMs) for natural language processing have been g...
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08/31/2023
Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models
Zero-shot referring image segmentation is a challenging task because it ...
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07/21/2023
Improving Transferability of Adversarial Examples via Bayesian Attacks
This paper presents a substantial extension of our work published at ICL...
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05/23/2023
Understanding Programs by Exploiting (Fuzzing) Test Cases
Semantic understanding of programs has attracted great attention in the ...
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04/26/2023
Improving Adversarial Transferability via Intermediate-level Perturbation Decay
Intermediate-level attacks that attempt to perturb feature representatio...
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02/10/2023
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
The transferability of adversarial examples across deep neural networks ...
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11/23/2022
Texts as Images in Prompt Tuning for Multi-Label Image Recognition
Prompt tuning has been employed as an efficient way to adapt large visio...
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10/14/2022
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Vision Transformers (ViTs) have recently achieved competitive performanc...
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05/23/2022
Collaborative Adversarial Training
The vulnerability of deep neural networks (DNNs) to adversarial examples...
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03/21/2022
An Intermediate-level Attack Framework on The Basis of Linear Regression
This paper substantially extends our work published at ECCV, in which an...
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03/06/2022
On Steering Multi-Annotations per Sample for Multi-Task Learning
The study of multi-task learning has drawn great attention from the comm...
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12/21/2021
A Theoretical View of Linear Backpropagation and Its Convergence
Backpropagation is widely used for calculating gradients in deep neural ...
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12/21/2021
Learned ISTA with Error-based Thresholding for Adaptive Sparse Coding
The learned iterative shrinkage thresholding algorithm (LISTA) introduce...
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06/30/2021
Robust Coreset for Continuous-and-Bounded Learning (with Outliers)
In this big data era, we often confront large-scale data in many machine...
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03/25/2021
Recent Advances in Large Margin Learning
This paper serves as a survey of recent advances in large margin trainin...
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03/25/2021
Deepfake Forensics via An Adversarial Game
With the progress in AI-based facial forgery (i.e., deepfake), people ar...
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12/07/2020
Backpropagating Linearly Improves Transferability of Adversarial Examples
The vulnerability of deep neural networks (DNNs) to adversarial examples...
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12/04/2020
Practical No-box Adversarial Attacks against DNNs
The study of adversarial vulnerabilities of deep neural networks (DNNs) ...
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10/17/2020
LID 2020: The Learning from Imperfect Data Challenge Results
Learning from imperfect data becomes an issue in many industrial applica...
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08/20/2020
Yet Another Intermediate-Level Attack
The transferability of adversarial examples across deep neural network (...
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07/04/2020
On Connections between Regularizations for Improving DNN Robustness
This paper analyzes regularization terms proposed recently for improving...
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06/10/2020
Towards Certified Robustness of Metric Learning
Metric learning aims to learn a distance metric such that semantically s...
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11/14/2019
Adversarial Margin Maximization Networks
The tremendous recent success of deep neural networks (DNNs) has sparked...
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06/11/2019
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
Unlike the white-box counterparts that are widely studied and readily ac...
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05/06/2019
Differentiable Architecture Search with Ensemble Gumbel-Softmax
For network architecture search (NAS), it is crucial but challenging to ...
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05/05/2019
Deep Discriminative Clustering Analysis
Traditional clustering methods often perform clustering with low-level i...
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04/19/2019
Deep Likelihood Network for Image Restoration with Multiple Degradations
Convolutional neural networks have been proven very effective in a varie...
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10/23/2018
Sparse DNNs with Improved Adversarial Robustness
Deep neural networks (DNNs) are computationally/memory-intensive and vul...
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02/23/2018
DeepDefense: Training Deep Neural Networks with Improved Robustness
Despite the efficacy on a variety of computer vision tasks, deep neural ...
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07/03/2017
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
In this paper, we propose an alternative method to estimate room layouts...
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06/07/2017
Network Sketching: Exploiting Binary Structure in Deep CNNs
Convolutional neural networks (CNNs) with deep architectures have substa...
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02/10/2017
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
This paper presents incremental network quantization (INQ), a novel meth...
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08/16/2016