Denis Gudovskiy | Researchers | Panasonic AI | Panasonic Research and Development on Artificial Intelligence (AI) - Panasonic

Denis Gudovskiy

Denis Gudovskiy

Denis Gudovskiy

M.Sc. in Computer Engineering
Panasonic AI Lab, Panasonic R&D Company of America

He specializes in deep learning-based algorithms for AI applications. His portfolio of research projects includes optimization of deep neural networks for edge AI devices, explainable AI tools, and automatic dataset management for computer vision applications.
In one of such projects he proposed and implemented a method of hardware-efficient neural network quantization and compression techniques for autonomous vehicles with stringent power and performance requirements. In the most recent work, Denis has demonstrated how data annotation costs could be significantly reduced with a use of advanced algorithms.His papers and demos are presented and published in top-tier machine learning and computer vision conferences such as NeurIPS, CVPR, ECCV, ICLR and ICASSP.
Within the explainable AI project, he has developed a tool to interpret and improve object detection results which contributes to Panasonic Automotive Company business. Denis sees corporate research as an important layer between moonshot academia projects and clearly-defined product development roadmaps in business units. His goal is to find and promote viable academia-grade opportunities at Panasonic within the exponentially growing landscape of AI applications.
In his free time, Denis enjoys riding motorcycles and mountaineering in Sierra Nevada.

*The department is where the interviewee belonged to at that time

Publications

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021

AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation

Denis Gudovskiy,Luca Rigazio, Shun Ishizaka,Kazuki Kozuka, Sotaro Tsukizawa
Link: https://arxiv.org/abs/2103.05863
2021/6/19
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision

Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Sotaro Tsukizawa
Link: https://arxiv.org/abs/2003.00393
NeurIPS 2019 demo track

Smart Home Appliances: Chat with Your Fridge

Denis Gudovskiy, Gyuri Han, Takuya Yamaguchi, Sotaro Tsukizawa
Link: https://arxiv.org/abs/1912.09589
ICLR The 2nd Learning from Limited Labeled Data (LLD) Workshop

Explanation-Based Attention for Semi-Supervised Deep Active Learning

Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Sotaro Tsukizawa
Link: https://openreview.net/forum?id=SyxKiVmedV
NeurIPS Workshop on Systems for ML and Open Source Software

Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions

Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Yasunori Ishii, Sotaro Tsukizawa
Link: https://arxiv.org/abs/1811.08011
ECCV 2nd International Workshop on Compact and Efficient Feature Representation and Learning in Computer Vision (CEFRL 2018)

DNN Feature Map Compression using Learned Representation over GF(2)

Denis Gudovskiy, Alec Hodgkinson, Luca Rigazio
Link: http://openaccess.thecvf.com/content_ECCVW_2018/papers/11132/Gudovskiy_DNN_Feature_Map_Compression_using_Learned_Representation_over_GF2_ECCVW_2018_paper.pdf