Complexity-Free Generalization via Distributionally Robust Optimization

Established approaches to obtain generalization bounds in data-driven optimization and machine learning mostly build on solutions from empirical risk minimization (ERM), which depend crucially on the functional complexity of the hypothesis class. In this paper, we present an alternate route to obtain these bounds on the solution from distributionally robust optimization (DRO), a recent data-driven optimization framework based on worst-case analysis and the notion of ambiguity set to capture statistical uncertainty. In contrast to the hypothesis class complexity in ERM, our DRO bounds depend on the ambiguity set geometry and its compatibility with the true loss function. Notably, when using maximum mean discrepancy as a DRO distance metric, our analysis implies, to the best of our knowledge, the first generalization bound in the literature that depends solely on the true loss function, entirely free of any complexity measures or bounds on the hypothesis class

Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation

Human face images usually appear with wide range of visual scales. The existing face representations pursue the bandwidth of handling scale variation via multi-scale scheme that assembles a finite series of predefined scales. Such multi-shot scheme brings inference burden, and the predefined scales inevitably have gap from real data. Instead, learning scale parameters from data, and using them for one-shot feature inference, is a decent solution. To this end, we reform the conv layer by resorting to the scale-space theory, and achieve two-fold facilities: 1) the conv layer learns a set of scales from real data distribution, each of which is fulfilled by a conv kernel; 2) the layer automatically highlights the feature at the proper channel and location corresponding to the input pattern scale and its presence. Then, we accomplish the hierarchical scale attention by stacking the reformed layers, building a novel style named SCale AttentioN Conv Neural Network (\textbf{SCAN-CNN}). We apply SCAN-CNN to the face recognition task and push the frontier of SOTA performance. The accuracy gain is more evident when the face images are blurry. Meanwhile, as a single-shot scheme, the inference is more efficient than multi-shot fusion. A set of tools are made to ensure the fast training of SCAN-CNN and zero increase of inference cost compared with the plain CNN

A unified 4/8/16/32-point integer IDCT architecture for multiple video coding standards

(4096x2048) 30fps video sequence at 191MHz working frequency, with 93K gate count and 18944-bit SRAM. We suggest a normalized criterion called design efficiency to compare with previous works. It shows that this design is 31% more efficient than previous work

Oxygen-defective Co 3 O 4 for pseudo-capacitive lithium storage

Abstract(#br)Transition metal oxide is widely studied type of high-capacity anode material for lithium ion batteries. Herein, oxygen-defective cobalt oxide with attractive lithium storage performance is prepared via a two-step strategy. Experimental results shows that there is certain amount of oxygen vacancies in Co 3 O 4 . Reversible conversion between metallic Co and CoO during the charge-discharge process was revealed by ex-situ XRD. Reversible morphology evolution is also confirmed by the ex-situ FE-SEM. The oxygen-defective Co 3 O 4 anode shows attractive stability and rate performance. It possesses a discharge capacity of 1006 mAh∙g −1 in the first cycle, with a high initial Coulombic efficiency of 73.9%. A reversible capacity of 896 mAh∙g −1 can be maintained after 200 cycles at 250 mA g −1 . It could even stably operate at an elevated current density of 5000 mA g −1 for 500 times. Further kinetic analysis reveals that pseudo-capacitance plays a dominant role in the lithium storage of oxygen-defective Co 3 O 4 . Existence of oxygen vacancies could not only facilitate Li + migration but also enhance electric conductivity to a certain extent, resulting in improved lithium storage performance