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08/21/2023
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection
Multi-view 3D detection based on BEV (bird-eye-view) has recently achiev...
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02/08/2023
Q-Diffusion: Quantizing Diffusion Models
Diffusion models have achieved great success in synthesizing diverse and...
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01/18/2023
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble
Ensemble learning has gain attention in resent deep learning research as...
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12/06/2022
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification
Mixed-precision quantization has been widely applied on deep neural netw...
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11/29/2022
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
The complicated architecture and high training cost of vision transforme...
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11/23/2021
HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance
With the recent demand of deploying neural network models on mobile and ...
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10/10/2021
NViT: Vision Transformer Compression and Parameter Redistribution
Transformers yield state-of-the-art results across many tasks. However, ...
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03/17/2021
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
We design blackbox transfer-based targeted adversarial attacks for an en...
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02/20/2021
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
Mixed-precision quantization can potentially achieve the optimal tradeof...
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12/08/2020
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Federated learning (FL) is a popular distributed learning framework that...
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09/30/2020
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Recent research finds CNN models for image classification demonstrate ov...
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05/23/2020
TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework with Anonymized Intermediate Representations
The success of deep learning partially benefits from the availability of...
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04/20/2020
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification
Modern deep neural networks (DNNs) often require high memory consumption...
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09/18/2019
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment
Emerging resistive random-access memory (ReRAM) has recently been intens...
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09/09/2019
DeepObfuscator: Adversarial Training Framework for Privacy-Preserving Image Classification
Deep learning has been widely utilized in many computer vision applicati...
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08/27/2019
DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures
In seeking for sparse and efficient neural network models, many previous...
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06/05/2018
AdvDetPatch: Attacking Object Detectors with Adversarial Patches
Object detectors have witnessed great progress in recent years and have ...
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06/05/2018