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One-Shot Domain Incremental Learning
In DIL, we assume that samples on new domains are observed over time.
Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
We propose an accuracy-preserving calibration method using the Concrete distribution as the probabilistic model on the probability simplex.
StyleDiff: Attribute Comparison Between Unlabeled Datasets in Latent Disentangled Space
One major challenge in machine learning applications is coping with mismatches between the datasets used in the development and those obtained in real-world applications.
Theoretical Analysis of the Advantage of Deepening Neural Networks
We propose two new criteria to understand the advantage of deepening neural networks.
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