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Found 10 papers, 1 papers with code
Learning Prototype via Placeholder for Zero-shot Recognition
Specifically, we combine seen classes to hallucinate new classes which play as placeholders of the unseen classes in the visual and semantic space.
Hidden Technical Debts for Fair Machine Learning in Financial Services
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.
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning
This paper investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks.
One-Shot Imitation Filming of Human Motion Videos
Imitation learning has been applied to mimic the operation of a human cameraman in several autonomous cinematography systems.
Generating Fair Universal Representations using Adversarial Models
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.
Learning to Film From Professional Human Motion Videos
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.
Generative Adversarial Models for Learning Private and Fair Representations
We present Generative Adversarial Privacy and Fairness (GAPF), a data-driven framework for learning private and fair representations of the data.
Generative Adversarial Privacy
We present a data-driven framework called generative adversarial privacy (GAP).
Context-Aware Generative Adversarial Privacy
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.
Local Feature Descriptor Learning with Adaptive Siamese Network
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.
