  Whether it is computer vision, natural language processing or speech
recognition, the essence of these applications is to obtain powerful feature
representations that make downstream applications completion more efficient.
Taking image recognition as an example, whether it is hand-crafted low-level
feature representation or feature representation extracted by a convolutional
neural networks(CNNs), the goal is to extract features that better represent
image features, thereby improving classification accuracy. However, we observed
that image feature representations share a large common vector and a few top
dominating directions. To address this problems, we propose a simple but
effective postprocessing method to render off-the-shelf feature representations
even stronger by eliminating the common mean vector from off-the-shelf feature
representations. The postprocessing is empirically validated on a variety of
datasets and feature extraction methods.such as VGG, LBP, and HOG. Some
experiments show that the features that have been post-processed by
postprocessing algorithm can get better results than original ones.
