Abstract
Increased availability of large-scale datasets has attracted increased academic and industrial attention to large-scale learning. Concurrently, huge growth in demand for smart phones has had a commensurate impact on related industries such as wafer manufacturing and mobile application industries. In the wafer manufacturing industry, increased demand has driven efforts to increase wafer production capacity, in part by reducing failure rates. Wafer map failure pattern recognition (WMFPR), an application of machine vision, can be used to automatically classify wafers, thus assisting engineers in identifying root causes of failure and thus increasing wafer yield. In the mobile application industry, increased demand for online music distribution has driven interest in music genre classification (MGC), which is an application of machine hearing, can facilitate music organization and music recommendation for online music services. However, reduced yet discriminative feature representations are still needed for these two large-scale learning applications. By contrast to conventional approaches, we consider an alternate approach for designing visual features for WMFPR and MGC. To validate system performance, we collected the world's largest public wafer map dataset (WM-811k) for WMFPR, and applied the world's largest benchmark dataset (MASD) for MGC. Experimental results show that the proposed visual features can considerably improve recognition rates. Furthermore, TSMC has adopted the proposed WMFPR method, while the proposed MGC method won the MIREX music genre classification contests from 2011 to 2013, indicating the robustness of the proposed methods.