Robust data representations for visual learning
Sheng Li · 2017
Extracting informative representations from data is a critical task in visual learning applications, which mitigates the gap between low-level observed data and high-level semantic knowledge. Many traditional visual learning algorithms pose strong assumptions on the underlying distribution of data. In practice, however, the data might be corrupted, contaminated with severe noise, or captured by different types of sensors, which violates these assumptions. As a result, it is of great importance to learn robust data representations that could effectively and efficiently handle the noisy visual data.