Peter Vajda

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Unbiased Teacher for Semi-Supervised Object Detection

Semi-supervised learning, i.e., training networks with both labeled and ...
02/18/2021 ∙ by Yen-Cheng Liu, et al. ∙ 15 ∙ share

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FBWave: Efficient and Scalable Neural Vocoders for Streaming Text-To-Speech on the Edge

Nowadays more and more applications can benefit from edge-based text-to-...
11/25/2020 ∙ by Bichen Wu, et al. ∙ 0 ∙ share

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One Shot 3D Photography

3D photography is a new medium that allows viewers to more fully experie...
08/27/2020 ∙ by Johannes Kopf, et al. ∙ 10 ∙ share

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Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild

We present a novel 3D pose refinement approach based on differentiable r...
07/17/2020 ∙ by Alexander Grabner, et al. ∙ 0 ∙ share

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Visual Transformers: Token-based Image Representation and Processing for Computer Vision

Computer vision has achieved great success using standardized image repr...
06/05/2020 ∙ by Bichen Wu, et al. ∙ 23 ∙ share

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FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function

Neural Architecture Search (NAS) yields state-of-the-art neural networks...
06/03/2020 ∙ by Xiaoliang Dai, et al. ∙ 11 ∙ share

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FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

Differentiable Neural Architecture Search (DNAS) has demonstrated great ...
04/12/2020 ∙ by Alvin Wan, et al. ∙ 17 ∙ share

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Deep Space-Time Video Upsampling Networks

Video super-resolution (VSR) and frame interpolation (FI) are traditiona...
04/06/2020 ∙ by Jaeyeon Kang, et al. ∙ 2 ∙ share

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SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

LiDAR point-cloud segmentation is an important problem for many applicat...
04/03/2020 ∙ by Chenfeng Xu, et al. ∙ 4 ∙ share

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Learning the Loss Functions in a Discriminative Space for Video Restoration

With more advanced deep network architectures and learning schemes such ...
03/20/2020 ∙ by Younghyun Jo, et al. ∙ 16 ∙ share

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Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

Many automated processes such as auto-piloting rely on a good semantic s...
07/16/2019 ∙ by Dmitrii Marin, et al. ∙ 1 ∙ share

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Learning to Generate Grounded Image Captions without Localization Supervision

When generating a sentence description for an image, it frequently remai...
06/01/2019 ∙ by Chih-Yao Ma, et al. ∙ 0 ∙ share

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Precision Highway for Ultra Low-Precision Quantization

Neural network quantization has an inherent problem called accumulated q...
12/24/2018 ∙ by Eunhyeok Park, et al. ∙ 0 ∙ share

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ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

This paper proposes an efficient neural network (NN) architecture design...
12/21/2018 ∙ by Xiaoliang Dai, et al. ∙ 0 ∙ share

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FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Designing accurate and efficient ConvNets for mobile devices is challeng...
12/09/2018 ∙ by Bichen Wu, et al. ∙ 0 ∙ share

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Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search

Recent work in network quantization has substantially reduced the time a...
11/30/2018 ∙ by Bichen Wu, et al. ∙ 0 ∙ share

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Value-aware Quantization for Training and Inference of Neural Networks

We propose a novel value-aware quantization which applies aggressively r...
04/20/2018 ∙ by Eunhyeok Park, et al. ∙ 0 ∙ share

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DSD: Dense-Sparse-Dense Training for Deep Neural Networks

Modern deep neural networks have a large number of parameters, making th...
07/15/2016 ∙ by Song Han, et al. ∙ 0 ∙ share

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Peter Vajda

Featured Co-authors

Masayoshi Tomizuka

Kurt Keutzer

Joseph E. Gonzalez

Marcus Rohrbach

Ghassan AlRegib

Vincent Lepetit

Song Han

Yuandong Tian

Bryan Catanzaro

Tong Xiao

Zsolt Kira

Unbiased Teacher for Semi-Supervised Object Detection

FBWave: Efficient and Scalable Neural Vocoders for Streaming Text-To-Speech on the Edge

One Shot 3D Photography

Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild

Visual Transformers: Token-based Image Representation and Processing for Computer Vision

FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

Deep Space-Time Video Upsampling Networks

SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

Learning the Loss Functions in a Discriminative Space for Video Restoration

Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

Learning to Generate Grounded Image Captions without Localization Supervision

Precision Highway for Ultra Low-Precision Quantization

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search

Value-aware Quantization for Training and Inference of Neural Networks

DSD: Dense-Sparse-Dense Training for Deep Neural Networks