  Training of Convolutional Neural Networks (CNNs) with data with noisy labels
is known to be a challenge. Based on the fact that directly providing the label
to the data (Positive Learning; PL) has a risk of allowing CNNs to memorize the
contaminated labels for the case of noisy data, the indirect learning approach
that uses complementary labels (Negative Learning for Noisy Labels; NLNL) has
proven to be highly effective in preventing overfitting to noisy data as it
reduces the risk of providing faulty target. NLNL further employs a three-stage
pipeline to improve convergence. As a result, filtering noisy data through the
NLNL pipeline is cumbersome, increasing the training cost. In this study, we
propose a novel improvement of NLNL, named Joint Negative and Positive Learning
(JNPL), that unifies the filtering pipeline into a single stage. JNPL trains
CNN via two losses, NL+ and PL+, which are improved upon NL and PL loss
functions, respectively. We analyze the fundamental issue of NL loss function
and develop new NL+ loss function producing gradient that enhances the
convergence of noisy data. Furthermore, PL+ loss function is designed to enable
faster convergence to expected-to-be-clean data. We show that the NL+ and PL+
train CNN simultaneously, significantly simplifying the pipeline, allowing
greater ease of practical use compared to NLNL. With a simple semi-supervised
training technique, our method achieves state-of-the-art accuracy for noisy
data classification based on the superior filtering ability.
