Deflation and Monetary Policy in Taiwan

From 1999 to 2003, Taiwan faced a deflationary situation. The reasons for this deflation can be attributed to both domestic and global factors. Domestic changes including local political unrest, tensions with China, outbound investment to China, a weakened financial system, and a deteriorating government financial situation, provided the backdrop for the economic slowdown and corresponding deflation. A number of global factors, especially the bursting of the Internet and IT bubbles in late 2000 and the rise of China's economy, also heavily influenced both global and Taiwanese prices. This paper adopts a simplified aggregate demand and aggregate supply model to derive a deterministic equation of the GDP deflator (PGDP), and then applies quarterly data covering the period from 1982 to 2003 to estimate the PGDP equation using 2SLS. The empirical results are used to identify the sources of PGDP deflation in Taiwan. In addition, the phenomenon of price divergence appears since 2002 where the WPI increased and the CPI decreased. The causes of the WPI-CPI divergence are also investigated in this paper.

Unsupervised Triplet Hashing for Fast Image Retrieval

Hashing has played a pivotal role in large-scale image retrieval. With the development of Convolutional Neural Network (CNN), hashing learning has shown great promise. But existing methods are mostly tuned for classification, which are not optimized for retrieval tasks, especially for instance-level retrieval. In this study, we propose a novel hashing method for large-scale image retrieval. Considering the difficulty in obtaining labeled datasets for image retrieval task in large scale, we propose a novel CNN-based unsupervised hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised hashing network is designed under the following three principles: 1) more discriminative representations for image retrieval; 2) minimum quantization loss between the original real-valued feature descriptors and the learned hash codes; 3) maximum information entropy for the learned hash codes. Extensive experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH outperforms several state-of-the-art unsupervised hashing methods in terms of retrieval accuracy