Search Results for author: Kazuki Kozuka

Found 8 papers, 5 papers with code

Refine and Represent: Region-to-Object Representation Learning

1 code implementation25 Aug 2022 Akash Gokul, Konstantinos Kallidromitis, Shufan Li, Yusuke Kato, Kazuki Kozuka, Trevor Darrell, Colorado J Reed

Recent works in self-supervised learning have demonstrated strong performance on scene-level dense prediction tasks by pretraining with object-centric or region-based correspondence objectives.

Representation Learning Self-Supervised Learning +2

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.