527 research outputs found
Cross-domain Few-shot Segmentation with Transductive Fine-tuning
Few-shot segmentation (FSS) expects models trained on base classes to work on
novel classes with the help of a few support images. However, when there exists
a domain gap between the base and novel classes, the state-of-the-art FSS
methods may even fail to segment simple objects. To improve their performance
on unseen domains, we propose to transductively fine-tune the base model on a
set of query images under the few-shot setting, where the core idea is to
implicitly guide the segmentation of query images using support labels.
Although different images are not directly comparable, their class-wise
prototypes are desired to be aligned in the feature space. By aligning query
and support prototypes with an uncertainty-aware contrastive loss, and using a
supervised cross-entropy loss and an unsupervised boundary loss as
regularizations, our method could generalize the base model to the target
domain without additional labels. We conduct extensive experiments under
various cross-domain settings of natural, remote sensing, and medical images.
The results show that our method could consistently and significantly improve
the performance of prototypical FSS models in all cross-domain tasks.Comment: 12 pages, 8 figure
Best Arm Identification with Fairness Constraints on Subpopulations
We formulate, analyze and solve the problem of best arm identification with
fairness constraints on subpopulations (BAICS). Standard best arm
identification problems aim at selecting an arm that has the largest expected
reward where the expectation is taken over the entire population. The BAICS
problem requires that an selected arm must be fair to all subpopulations (e.g.,
different ethnic groups, age groups, or customer types) by satisfying
constraints that the expected reward conditional on every subpopulation needs
to be larger than some thresholds. The BAICS problem aims at correctly
identify, with high confidence, the arm with the largest expected reward from
all arms that satisfy subpopulation constraints. We analyze the complexity of
the BAICS problem by proving a best achievable lower bound on the sample
complexity with closed-form representation. We then design an algorithm and
prove that the algorithm's sample complexity matches with the lower bound in
terms of order. A brief account of numerical experiments are conducted to
illustrate the theoretical findings
Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks
Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected mosquitoes. Automation of the diagnosis process will enable accurate diagnosis of the disease and hence holds the promise of delivering reliable health-care to resource-scarce areas. Machine learning technologies have been used for automated diagnosis of malaria. We present some of our recent progresses on highly accurate classification of malaria-infected cells using deep convolutional neural networks. First, we describe image processing methods used for segmentation of red blood cells from wholeslide images. We then discuss the procedures of compiling a pathologists-curated image dataset for training deep neural network, as well as data augmentation methods used to significantly increase the size of the dataset, in light of the overfitting problem associated with training deep convolutional neural networks. We will then compare the classification accuracies obtained by deep convolutional neural networks through training, validating, and testing with various combinations of the datasets. These datasets include the original dataset and the significantly augmented datasets, which are obtained using direct interpolation, as well as indirect interpolation using automatically extracted features provided by stacked autoencoders. This chapter ends with a discussion of further research
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