The Rise of Mega-FTAs. EU Centre in Singpore Fact Sheet (October 2013)

Free trade agreements (FTAs) are negotiated so that countries and businesses can benefit from the possibilities that international economic integration offers. By eradicating barriers to trade, mutual gains can be generated. FTAs, in general, create a bigger market, increase competition, but at the same time reward economies of scale, which in turn induces companies and countries to allocate their time and resources more efficiently. Moreover, greater business competition arising from FTAs can catalyse economic reforms such as accelerating the adoption of existing technologies and stimulating the development of new ones (Krist, 2012)

Cross-convolutional-layer Pooling for Image Recognition

Recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large image dataset can be used as a universal image descriptor, and that doing so leads to impressive performance for a variety of image classification tasks. Most of these studies adopt activations from a single DCNN layer, usually the fully-connected layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive convolutional layers: one layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of convolutional layers, we further develop two schemes to realize this idea. The first one directly uses convolutional layers from a DCNN. The second one applies the pretrained CNN on densely sampled image regions and treats the fully-connected activations of each image region as convolutional feature activations. We then train another convolutional layer on top of that as the pooling-guidance convolutional layer. By applying our method to three popular visual classification tasks, we find our first scheme tends to perform better on the applications which need strong discrimination on subtle object patterns within small regions while the latter excels in the cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing ways of extracting image representations from a DCNN.Comment: Fixed typos. Journal extension of arXiv:1411.7466. Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc