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DAPlankton: Benchmark Dataset for Multi-instrument Plankton Recognition via Fine-grained Domain Adaptation
In this paper, we present a new DA dataset called DAPlankton which consists of phytoplankton images obtained with different instruments.
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies
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.
Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives
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.
Towards Phytoplankton Parasite Detection Using Autoencoders
Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples.
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
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.
