We use our MetaCC benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift, which can be controlled in the coding problem.
Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community.
Intercalation is an effective method to improve and modulate properties of two-dimensional materials.
Electron Microscopy Materials Science
Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks.
Therefore we propose an online shortest-path meta-learning framework that is both computationally tractable and practically effective for improving DA performance.
In DG this means encountering a sequence of domains and at each step training to maximise performance on the next domain.
The Large-Scale Pedestrian Retrieval Competition (LSPRC) mainly focuses on person retrieval which is an important end application in intelligent vision system of surveillance.
In this paper, we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime.
Instead there is a fundamental process of abstraction and iconic rendering, where overall geometry is warped and salient details are selectively included.
Contemporary deep learning techniques have made image recognition a reasonably reliable technology.
In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning.