Search Results for author: Denis Gudovskiy

Found 8 papers, 7 papers with code

MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer

1 code implementation3 May 2022 Jinze Yu, Jiaming Liu, Xiaobao Wei, Haoyi Zhou, Yohei Nakata, Denis Gudovskiy, Tomoyuki Okuno, JianXin Li, Kurt Keutzer, Shanghang Zhang

To solve this problem, we propose an end-to-end cross-domain detection Transformer based on the mean teacher framework, MTTrans, which can fully exploit unlabeled target domain data in object detection training and transfer knowledge between domains via pseudo labels.

Domain Adaptation object-detection +1

Contrastive Neural Processes for Self-Supervised Learning

1 code implementation24 Oct 2021 Konstantinos Kallidromitis, Denis Gudovskiy, Kazuki Kozuka, Iku Ohama, Luca Rigazio

In this paper, we propose a novel self-supervised learning framework that combines contrastive learning with neural processes.

Contrastive Learning Data Augmentation +2

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

3 code implementations27 Jul 2021 Denis Gudovskiy, Shun Ishizaka, Kazuki Kozuka

Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting.

Ranked #19 on Anomaly Detection on MVTec AD (using extra training data)

Unsupervised Anomaly Detection

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

1 code implementation CVPR 2021 Denis Gudovskiy, Luca Rigazio, Shun Ishizaka, Kazuki Kozuka, Sotaro Tsukizawa

To overcome these limitations, we reformulate AutoAugment as a generalized automated dataset optimization (AutoDO) task that minimizes the distribution shift between test data and distorted train dataset.

Smart Home Appliances: Chat with Your Fridge

2 code implementations19 Dec 2019 Denis Gudovskiy, Gyuri Han, Takuya Yamaguchi, Sotaro Tsukizawa

Current home appliances are capable to execute a limited number of voice commands such as turning devices on or off, adjusting music volume or light conditions.

Visual Reasoning

Explanation-Based Attention for Semi-Supervised Deep Active Learning

no code implementations ICLR Workshop LLD 2019 Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Sotaro Tsukizawa

We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.

Active Learning

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

1 code implementation19 Nov 2018 Denis Gudovskiy, Alec Hodgkinson, Takuya Yamaguchi, Yasunori Ishii, Sotaro Tsukizawa

We qualitatively and quantitatively show that the proposed explanation method can be used to find image features which cause failures in DNN object detection.

Feature Importance object-detection +1

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