Dequan Wang 王德泉
Dequan Wang 王德泉
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Back to the Source: Diffusion-Driven Adaptation to Test-Time Corruption
Our diffusion-driven adaptation method, DDA, shares its models for classification and generation across all domains. Both models are trained on the source domain, then fixed during testing. We augment diffusion with image guidance and self-ensembling to automatically decide how much to adapt.
Jin Gao
,
Jialing Zhang
,
Xihui Liu
,
Trevor Darrell
,
Evan Shelhamer
,
Dequan Wang
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GACT: Activation Compressed Training for Generic Network Architectures
GACT is an activation compression training (ACT) framework to support a broad range of machine learning tasks for generic neural network architectures with limited domain knowledge. By analyzing a linearized version of ACT’s approximate gradient, we prove the convergence of GACT without prior knowledge on operator type or model architecture.
Xiaoxuan Liu
,
Lianmin Zheng
,
Dequan Wang
,
Yukuo Cen
,
Weize Chen
,
Xu Han
,
Jianfei Chen
,
Zhiyuan Liu
,
Jie Tang
,
Joseph Gonzalez
,
Michael Mahoney
,
Alvin Cheung
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Contrastive Test-time Adaptation
We introduce AdaContrast, a novel test-time adaptation strategy that uses self-supervised contrastive learning on the target domain to exploit the pair-wise information among target samples, which is optimized jointly with pseudo labeling.
Dian Chen
,
Dequan Wang
,
Trevor Darrell
,
Sayna Ebrahimi
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Project
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
ActNN is a PyTorch library for memory-efficient training. It reduces the training memory footprint by compressing the saved activations. ActNN is implemented as a collection of memory-saving layers. These layers have an identical interface to their PyTorch counterparts.
Jianfei Chen
,
Lianmin Zheng
,
Zhewei Yao
,
Dequan Wang
,
Ion Stoica
,
Michael Mahoney
,
Joseph Gonzalez
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机器之心
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Co-design of a deformable convolution operation on FPGA with hardware-friendly modifications, showing up to 9.76× hardware speedup. Development of an efficient DNN model for object detection with co-designed input-adaptive deformable convolution that achieves 67.1 AP50 on Pascal VOC with 2.9 MB parameters. The model is 20.9× smaller but 10% more accurate than the Tiny-YOLO. Implementation of an FPGA accelerator to support the target neural network design that runs at 26 frames per second on Pascal VOC with 61.7 AP50.
Qijing Huang
,
Dequan Wang
,
Zheng Dong
,
Yizhao Gao
,
Yaohui Cai
,
Bichen Wu
,
Kurt Keutzer
,
John Wawrzynek
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Tent: Fully Test-time Adaptation by Entropy Minimization
Tent equips a model to adapt itself to new and different data during testing ☀️ 🌧 ❄️. Tented models adapt online and batch-by-batch to reduce error on dataset shifts like corruptions, simulation-to-real discrepancies, and other differences between training and testing data.
Dequan Wang
,
Evan Shelhamer
,
Shaoteng Liu
,
Bruno Olshausen
,
Trevor Darrell
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Video
Joint Monocular 3D Vehicle Detection and Tracking
We present a novel framework that jointly detects and tracks 3D vehicle bounding boxes. Our approach leverages 3D pose estimation to learn 2D patch association overtime and uses temporal information from tracking to obtain stable 3D estimation.
Hou-Ning Hu
,
Qi-Zhi Cai
,
Dequan Wang
,
Ji Lin
,
Min Sun
,
Philipp Krähenbühl
,
Trevor Darrell
,
Fisher Yu
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Monocular Plan View Networks for Autonomous Driving
Monocular Plan View Networks use a monocular plan view image together with a first-person image to learn a deep driving policy. The plan view image is generated from the first-person image using 3D detection and re-projection.
Dequan Wang
,
Coline Devin
,
Qi-Zhi Cai
,
Philipp Krähenbühl
,
Trevor Darrell
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Deep Object Centric Policies for Autonomous Driving
We propose an object-centric perception approach to deep control problems, and focus our experimentation on au- tonomous driving. Existing end-to-end models are holistic in nature; our approach augments policy learning with explicit representations that provide object-level attention.
Dequan Wang
,
Coline Devin
,
Qi-Zhi Cai
,
Fisher Yu
,
Trevor Darrell
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Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
We present a novel neural network layer that performs differentiable rasterization of arbitrary simplex-mesh-based geometrical signals (e.g., point clouds, line mesh, triangular mesh, tetrahedron mesh, polygon and polyhedron) of arbitrary dimensions. We further provide examples of incorporating the DDSL into neural networks for tasks such as polygonal image segmentation and neural shape optimization (for MNIST digits and airfoils).
Chiyu Jiang
,
Dequan Wang
,
Jingwei Huang
,
Philip Marcus
,
Matthias Nießner
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