no code implementations • 8 Feb 2024 • Daniel Batrakhanov, Tuomas Eerola, Kaisa Kraft, Lumi Haraguchi, Lasse Lensu, Sanna Suikkanen, María Teresa Camarena-Gómez, Jukka Seppälä, Heikki Kälviäinen
In this paper, we present a new DA dataset called DAPlankton which consists of phytoplankton images obtained with different instruments.
no code implementations • 12 Oct 2023 • Teemu Härkönen, Erik M. Vartiainen, Lasse Lensu, Matthew T. Moores, Lassi Roininen
We train two Bayesian neural networks to estimate parameters of the gamma process which can then be used to estimate the underlying Raman spectrum and simultaneously provide uncertainty through the estimation of parameters of a probability distribution.
no code implementations • 19 May 2023 • Tuomas Eerola, Daniel Batrakhanov, Nastaran Vatankhah Barazandeh, Kaisa Kraft, Lumi Haraguchi, Lasse Lensu, Sanna Suikkanen, Jukka Seppälä, Timo Tamminen, Heikki Kälviäinen
However, important challenges remain unsolved: 1) the domain shift between the datasets hindering the development of a general plankton recognition system that would work across different imaging instruments, 2) the difficulty to identify and process the images of previously unseen classes, and 3) the uncertainty in expert annotations that affects the training of the machine learning models for recognition.
no code implementations • 15 Mar 2023 • Simon Bilik, Daniel Batrakhanov, Tuomas Eerola, Lumi Haraguchi, Kaisa Kraft, Silke Van den Wyngaert, Jonna Kangas, Conny Sjöqvist, Karin Madsen, Lasse Lensu, Heikki Kälviäinen, Karel Horak
Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples.
no code implementations • 7 Jun 2016 • Huiling Wang, Tapani Raiko, Lasse Lensu, Tinghuai Wang, Juha Karhunen
We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data.