Specifically, we combine seen classes to hallucinate new classes which play as placeholders of the unseen classes in the visual and semantic space.
To identify hidden technical debts that exist in building fair ML system for Fintech, we focus on key pipeline stages including data preparation, model development, system monitoring and integration in production.
This paper investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks.
Imitation learning has been applied to mimic the operation of a human cameraman in several autonomous cinematography systems.
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori.
In this study, we propose a learning-based framework which incorporates the video contents and previous camera motions to predict the future camera motions that enable the capture of professional videos.
We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations of the data.
On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility.
Although the recent progress in the deep neural network has led to the development of learnable local feature descriptors, there is no explicit answer for estimation of the necessary size of a neural network.