A concise review of recent few-shot meta-learning methods

Few-shot meta-learning has been recently reviving with expectations to mimic humanity’s fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics. We conclude this review with some vital current challenges and future prospects in few-shot meta-learning

First-principles study of native point defects in Bi2Se3

Using first-principles method within the framework of the density functional theory, we study the influence of native point defect on the structural and electronic properties of Bi$_2$Se$_3$. Se vacancy in Bi$_2$Se$_3$ is a double donor, and Bi vacancy is a triple acceptor. Se antisite (Se$_{Bi}$) is always an active donor in the system because its donor level ($\varepsilon$(+1/0)) enters into the conduction band. Interestingly, Bi antisite(Bi$_{Se1}$) in Bi$_2$Se$_3$ is an amphoteric dopant, acting as a donor when $\mu$$_e$$<$0.119eV (the material is typical p-type) and as an acceptor when $\mu$$_e$$>$0.251eV (the material is typical n-type). The formation energies under different growth environments (such as Bi-rich or Se-rich) indicate that under Se-rich condition, Se$_{Bi}$ is the most stable native defect independent of electron chemical potential $\mu$$_e$. Under Bi-rich condition, Se vacancy is the most stable native defect except for under the growth window as $\mu$$_e$$>$0.262eV (the material is typical n-type) and $\Delta$$\mu$$_{Se}$$<$-0.459eV(Bi-rich), under such growth windows one negative charged Bi$_{Se1}$ is the most stable one.Comment: 7 pages, 4 figure

Amortized Bayesian prototype meta-learning: A new probabilistic meta-learning approach to few-shot image classification

Probabilistic meta-learning methods recently have achieved impressive success in few-shot image classification. However, they introduce a huge number of random variables for neural network weights and thus severe computational and inferential challenges. In this paper, we propose a novel probabilistic meta-learning method called amortized Bayesian prototype meta-learning. In contrast to previous methods, we introduce only a small number of random variables for latent class prototypes rather than a huge number for network weights; we learn to learn the posterior distributions of these latent prototypes in an amortized inference way with no need for an extra amortization network, such that we can easily approximate their posteriors conditional on few labeled samples, whenever at meta-training or meta-testing stage. The proposed method can be trained end-to-end without any pre-training. Compared with other probabilistic meta-learning methods, our proposed approach is more interpretable with much less random variables, while still be able to achieve competitive performance for few-shot image classification problems on various benchmark datasets. Its excellent robustness and predictive uncertainty are also demonstrated through ablation studies

Enhance Via Decoupling: Improving Multi-Label Classifiers With Variational Feature Augmentation

Multi-label classification remains a challenging problem due to the inherent label imbalance issue, which brings overfitting of minor categories to modern deep models. In this paper, to tackle this issue, we propose a novel method named Variational Feature Augmentation (VFA) to enhance the deep neural networks for multi-label classification. Our method decouples the feature vectors extracted by the backbone network into multiple low-dimensional spaces via a novely proposed Variational Feature Decoupling Module. The decoupled feature vectors are then re-combined with a shuffle operation and a Feature Augmentation Layer to enrich the minor co-occurrence relations, mitigating the label imbalance. Different from most other methods, VFA does not modify the network architecture or introduce extra computation cost in inference phase. We conduct comprehensive experiments on four benchmarks of two visual multi-label classification tasks, pedestrian attribute recognition and multi-label image recognition, and the results demonstrate the effectiveness and generality of the proposed VFA

A New Object Scene Flow Algorithm Based on Support Points Selection and Robust Moving Object Proposal

Recent algorithms of object scene flow estimation suffer from low computational efficiency or unstable moving object proposals. To tackle these two problems simultaneously, in this paper we propose a new, efficient and robust algorithm for object scene flow estimation, through making two technical contributions. Firstly to improve the efficiency, we propose to select only a few pixels termed support points for matching cost calculation rather than using all pixels. The support points are defined as those pixels with high confidence in feature matching. Secondly to attain stable moving object proposals, we propose a motion magnitude-adaptive thresholding scheme for ego-motion outlier detection, after patch matching on CNN-extracted high quality features. These two contributions, though simple, ensure a remarkable improvement in both efficiency and accuracy from the original object scene flow method, as well as making the proposed algorithm a strong practicable alternative to much more sophisticated state-of-the-art competitors

Effects of Lactic Acid Bacteria on the Quality of \u3cem\u3eAchnatherum splendens\u3c/em\u3e Silage

Achnatherum splendens is an important forage for ruminant animals, but it has a high fiber content, and there is little information about the quality of Achnatherum splendens silage. This experiment was undertaken to study the effects of lactic acid bacteria on the quality of Achnatherum splendens (AS) silage

Exposure to famine in early life and the risk of obesity in adulthood in Qingdao : Evidence from the 1959-1961 Chinese famine

Background and aims: We aimed to evaluate the association between famine exposure during early life and obesity and obesity(max) (obese at the highest weight) in adulthood. Methods and results: Data were from two population-based cross-sectional surveys conducted in 2006 and 2009 in Qingdao, China. A total of 8185 subjects born between 1/1/1941 and 12/31/1971 were categorized into unexposed (born between 01/01/1962 and 12/31/1971), fetal/ infant exposed (born between 01/01/1959 and 12/31/1961), childhood exposed (born between 01/01/1949 and 12/31/1958) and adolescence exposed (born between 01/01/1941 and 12/31/1948) according to their age when exposed to the Chinese famine from 1959 to 1961. Obesity was defined as BMI (body mass index) >= 28.0 and obesitymax was defined as BMImax (BMI at the highest weight) >= 28.0. We compared fetal/ infant exposed, childhood exposed and adolescence exposed to the unexposed using logistic regression models to assess the effect of famine exposure on later obesity and obesitymax. Fetal/infant exposed (OR = 1.59, P <0.001), childhood exposed (OR = 1.42, P <0.01) and adolescence exposed (OR = 1.86, P <0.01) all had higher risks of obesity than the unexposed. Exposure groups were more likely to be obese at their highest weight than the unexposed, and ORs (95%CIs) for obesitymax in the fetal/ infant exposed, childhood exposed and adolescence exposed were 1.49(1.20e1.86), 1.24(1.02e1.49) and 1.64 (1.40 e1.93), respectively. Similar results were found in both men and women. Conclusion: Exposure to famine in early life was associated with increased risks of obesity and obesitymax in adulthood. Preventing undernutrition in early life appears beneficial to reduce the prevalence of later obesity. (C) 2016 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.Peer reviewe