Search Results for author: Huanrui Yang

Found 25 papers, 13 papers with code

Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation

1 code implementation26 May 2024 Rongyu Zhang, Aosong Cheng, Yulin Luo, Gaole Dai, Huanrui Yang, Jiaming Liu, ran Xu, Li Du, Yuan Du, Yanbing Jiang, Shanghang Zhang

Continual Test-Time Adaptation (CTTA), which aims to adapt the pre-trained model to ever-evolving target domains, emerges as an important task for vision models.

Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning

no code implementations13 Apr 2024 Yijiang Liu, Rongyu Zhang, Huanrui Yang, Kurt Keutzer, Yuan Du, Li Du, Shanghang Zhang

Large Language Models (LLMs) have demonstrated significant potential in performing multiple tasks in multimedia applications, ranging from content generation to interactive entertainment, and artistic creation.

Magic-Me: Identity-Specific Video Customized Diffusion

1 code implementation14 Feb 2024 Ze Ma, Daquan Zhou, Chun-Hsiao Yeh, Xue-She Wang, Xiuyu Li, Huanrui Yang, Zhen Dong, Kurt Keutzer, Jiashi Feng

To achieve this, we propose three novel components that are essential for high-quality identity preservation and stable video generation: 1) a noise initialization method with 3D Gaussian Noise Prior for better inter-frame stability; 2) an ID module based on extended Textual Inversion trained with the cropped identity to disentangle the ID information from the background 3) Face VCD and Tiled VCD modules to reinforce faces and upscale the video to higher resolution while preserving the identity's features.

Text-to-Image Generation Video Generation

Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting

no code implementations14 Dec 2023 Anthony Chen, Huanrui Yang, Yulu Gan, Denis A Gudovskiy, Zhen Dong, Haofan Wang, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, Shanghang Zhang

In particular, we build a tree-like Split-Ensemble architecture by performing iterative splitting and pruning from a shared backbone model, where each branch serves as a submodel corresponding to a subtask.

Q-Diffusion: Quantizing Diffusion Models

1 code implementation ICCV 2023 Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, Kurt Keutzer

We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture of the diffusion models, which compresses the noise estimation network to accelerate the generation process.

Image Generation Noise Estimation +1

HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble

no code implementations18 Jan 2023 Jingchi Zhang, Huanrui Yang, Hai Li

We propose a new prespective on exploring the intrinsic diversity within a model architecture to build efficient DNN ensemble.

Ensemble Learning Model Compression +1

CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification

no code implementations6 Dec 2022 Lirui Xiao, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, Shanghang Zhang

CSQ stabilizes the bit-level mixed-precision training process with a bi-level gradual continuous sparsification on both the bit values of the quantized weights and the bit selection in determining the quantization precision of each layer.

Quantization

NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers

no code implementations CVPR 2023 Yijiang Liu, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, Shanghang Zhang

Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer.

Quantization

HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance

1 code implementation23 Nov 2021 Huanrui Yang, Xiaoxuan Yang, Neil Zhenqiang Gong, Yiran Chen

We therefore propose HERO, a Hessian-enhanced robust optimization method, to minimize the Hessian eigenvalues through a gradient-based training process, simultaneously improving the generalization and quantization performance.

Quantization

Global Vision Transformer Pruning with Hessian-Aware Saliency

1 code implementation CVPR 2023 Huanrui Yang, Hongxu Yin, Maying Shen, Pavlo Molchanov, Hai Li, Jan Kautz

This work aims on challenging the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage, where we redistribute the parameters both across transformer blocks and between different structures within the block via the first systematic attempt on global structural pruning.

Efficient ViTs Philosophy

Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective

1 code implementation CVPR 2021 Jingwei Sun, Ang Li, Binghui Wang, Huanrui Yang, Hai Li, Yiran Chen

The key idea of our defense is learning to perturb data representation such that the quality of the reconstructed data is severely degraded, while FL performance is maintained.

Federated Learning Inference Attack

Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?

no code implementations17 Mar 2021 Nathan Inkawhich, Kevin J Liang, Jingyang Zhang, Huanrui Yang, Hai Li, Yiran Chen

During the online phase of the attack, we then leverage representations of highly related proxy classes from the whitebox distribution to fool the blackbox model into predicting the desired target class.

BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

1 code implementation ICLR 2021 Huanrui Yang, Lin Duan, Yiran Chen, Hai Li

Mixed-precision quantization can potentially achieve the optimal tradeoff between performance and compression rate of deep neural networks, and thus, have been widely investigated.

Neural Architecture Search Quantization

Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

4 code implementations8 Dec 2020 Jingwei Sun, Ang Li, Binghui Wang, Huanrui Yang, Hai Li, Yiran Chen

In this work, we show our key observation that the data representation leakage from gradients is the essential cause of privacy leakage in FL.

Federated Learning

TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations

no code implementations23 May 2020 Ang Li, Yixiao Duan, Huanrui Yang, Yiran Chen, Jianlei Yang

The goal of this framework is to learn a feature extractor that can hide the privacy information from the intermediate representations; while maximally retaining the original information embedded in the raw data for the data collector to accomplish unknown learning tasks.

Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

1 code implementation20 Apr 2020 Huanrui Yang, Minxue Tang, Wei Wen, Feng Yan, Daniel Hu, Ang Li, Hai Li, Yiran Chen

In this work, we propose SVD training, the first method to explicitly achieve low-rank DNNs during training without applying SVD on every step.

Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment

1 code implementation18 Sep 2019 Jingyang Zhang, Huanrui Yang, Fan Chen, Yitu Wang, Hai Li

However, the power hungry analog-to-digital converters (ADCs) prevent the practical deployment of ReRAM-based DNN accelerators on end devices with limited chip area and power budget.

DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones

no code implementations9 Sep 2019 Ang Li, Jiayi Guo, Huanrui Yang, Flora D. Salim, Yiran Chen

Our experiments on CelebA and LFW datasets show that the quality of the reconstructed images from the obfuscated features of the raw image is dramatically decreased from 0. 9458 to 0. 3175 in terms of multi-scale structural similarity.

General Classification Image Classification +3

DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures

1 code implementation ICLR 2020 Huanrui Yang, Wei Wen, Hai Li

Inspired by the Hoyer measure (the ratio between L1 and L2 norms) used in traditional compressed sensing problems, we present DeepHoyer, a set of sparsity-inducing regularizers that are both differentiable almost everywhere and scale-invariant.

Efficient Neural Network

DPatch: An Adversarial Patch Attack on Object Detectors

1 code implementation5 Jun 2018 Xin Liu, Huanrui Yang, Ziwei Liu, Linghao Song, Hai Li, Yiran Chen

Successful realization of DPatch also illustrates the intrinsic vulnerability of the modern detector architectures to such patch-based adversarial attacks.

Object

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

no code implementations27 May 2017 Chang Song, Hsin-Pai Cheng, Huanrui Yang, Sicheng Li, Chunpeng Wu, Qing Wu, Hai Li, Yiran Chen

Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.

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