Search Results for author:
Found 7 papers, 4 papers with code
ImbaGCD: Imbalanced Generalized Category Discovery
Therefore, we present a challenging and practical problem, Imbalanced Generalized Category Discovery (ImbaGCD), where the distribution of unlabeled data is imbalanced, with known classes being more frequent than unknown ones.
Supervised Knowledge May Hurt Novel Class Discovery Performance
Next, by using the proposed transfer flow, we conduct various empirical experiments with different levels of semantic similarity, yielding that supervised knowledge may hurt NCD performance.
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation
We apply the proposed method to the MNIST dataset and the MIT-BIH dataset with a convolutional auto-encoder.
A Closer Look at Novel Class Discovery from the Labeled Set
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes.
RankSEG: A Consistent Ranking-based Framework for Segmentation
In this paper, we establish a theoretical foundation of segmentation with respect to the Dice/IoU metrics, including the Bayes rule and Dice-/IoU-calibration, analogous to classification-calibration or Fisher consistency in classification.
Two-level monotonic multistage recommender systems
In this article, we develop a multistage recommender system utilizing a two-level monotonic property characterizing a monotonic chain of events for personalized prediction.
Significance tests of feature relevance for a black-box learner
In this article, we derive one-split and two-split tests relaxing the assumptions and computational complexity of existing black-box tests and extending to examine the significance of a collection of features of interest in a dataset of possibly a complex type such as an image.
