I Have Seen Enough: Transferring Parts Across Categories
By David Novotný, Diane Larlus, Andrea Vedaldi. 2016
23 citations recorded by the source corpus.
Subfields: Clinical Psychology, Education, Experimental and Cognitive Psychology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Clinical Psychology
In Education
In Experimental and Cognitive Psychology
Citations in the corpus
- Interpretable Explanations of Black Boxes by Meaningful Perturbation (2017, methodology): “The principled interpretability of our method also allows us to identify instances when an algorithm may have learned the wrong association.”
- Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “On the other hand, the relation between DA and domain generalization [151, 254, 97, 94, 155], multi-task learning [26, 76, 178] or few-shot learning [149, 80] is much stronger.”
- R-FCN-3000 at 30fps: Decoupling Detection and Classification (2017, methodology): “For classifying and localizing a large number of classes, some methods leverage the fact that parts can be shared across objects categories [27, 32, 37, 28].”
- What leads to generalization of object proposals? (2020, background): “Generalizing localization across multiple classes: The idea of different object classes sharing the same structure has been exploited in building detection models for a long time[5,22,23,29,34].”
- Deep Domain Generalization With Structured Low-Rank Constraint (2018, background): “Thus, conventional domain adaptation techniques [3]–[7], [9]–[11] would fail in such cases, since they all need target data to guide knowledge transfer during model training.”