  This paper considers recognizing products from daily photos, which is an
important problem in real-world applications but also challenging due to
background clutters, category diversities, noisy labels, etc. We address this
problem by two contributions. First, we introduce a novel large-scale product
image dataset, termed as Product-90. Instead of collecting product images by
labor-and time-intensive image capturing, we take advantage of the web and
download images from the reviews of several e-commerce websites where the
images are casually captured by consumers. Labels are assigned automatically by
the categories of e-commerce websites. Totally the Product-90 consists of more
than 140K images with 90 categories. Due to the fact that consumers may upload
unrelated images, it is inevitable that our Product-90 introduces noisy labels.
As the second contribution, we develop a simple yet efficient \textit{guidance
learning} (GL) method for training convolutional neural networks (CNNs) with
noisy supervision. The GL method first trains an initial teacher network with
the full noisy dataset, and then trains a target/student network with both
large-scale noisy set and small manually-verified clean set in a multi-task
manner. Specifically, in the stage of student network training, the large-scale
noisy data is supervised by its guidance knowledge which is the combination of
its given noisy label and the soften label from the teacher network. We conduct
extensive experiments on our Products-90 and public datasets, namely Food101,
Food-101N, and Clothing1M. Our guidance learning method achieves performance
superior to state-of-the-art methods on these datasets.
