activity
20142024
most citedA systematic study of the class imbalance problem in convolutional neural networks

3.1k citations · 3.3k across the 19 of their papers we have counts for

collaborators

19 papers

cs.CV2024★ 25 cited

Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset

Xiaomeng Zhu, Talha Bilal, Pär Mårtensson +3

This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap…

cs.CV2023

Noisy Image Segmentation With Soft-Dice

Marcus Nordström, Henrik Hult, Atsuto Maki +1

This paper presents a study on the soft-Dice loss, one of the most popular loss functions in medical image segmentation, for situations where noise is present in target labels. In…

cs.LG2023

Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces

Takumi Kanai, Naoya Sogi, Atsuto Maki +1

This paper proposes a new method for anomaly detection in time-series data by incorporating the concept of difference subspace into the singular spectrum analysis (SSA). The key id…

cs.CV2023

Marginal Thresholding in Noisy Image Segmentation

Marcus Nordström, Henrik Hult, Atsuto Maki

This work presents a study on label noise in medical image segmentation by considering a noise model based on Gaussian field deformations. Such noise is of interest because it yiel…

cs.CV2022

Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks

Miquel Martí i Rabadán, Alessandro Pieropan, Hossein Azizpour +1

We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks combining pseudo-labeling and consistency regularization via…

cs.CV2022

Towards a Unified View of Affinity-Based Knowledge Distillation

Vladimir Li, Atsuto Maki

Knowledge transfer between artificial neural networks has become an important topic in deep learning. Among the open questions are what kind of knowledge needs to be preserved for…