Emanuel Ben Baruch | Asymmetric Loss For Multi-Label Classification
Details
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Abstract:
Pictures of everyday life are inherently multi-label in nature.
Hence, multi-label classification is commonly used to analyze
their content. In typical multi-label datasets, each picture contains only a few positive labels, and many negative ones. This
positive-negative imbalance can result in under-emphasizing
gradients from positive labels during training, leading to poor
accuracy.
In this lecture, we will introduce a novel asymmetric loss (”ASL”),
that operates differently on positive and negative samples.
The loss dynamically down-weights the importance of easy
negative samples, causing the optimization process to focus
more on the positive samples, and also enables to discard mislabeled negative samples.
We demonstrate how ASL leads to a more ”balanced” network, with increased average probabilities for positive samples, and show how this balanced network is translated to better mAP scores, compared to commonly used losses. Furthermore, we offer a method that can dynamically adjust the level
of asymmetry throughout the training.
With ASL, we reach new state-of-the-art results on three
common multi-label datasets, including achieving 86.6% on
MS-COCO. We also demonstrate ASL applicability for other
tasks such as fine-grain single-label classification and object
detection.
ASL is effective, easy to implement, and does not increase
the training time or complexity. Public code will be available
Bio:
Emanuel Ben Baruch is an applied researcher at the Alibaba DAMO Academy, Machine intelligence Israel lab. His main fields of interests are deep learning approaches for image understanding as multi-label classification and object detection. Before joining Alibaba, Emanuel worked as a Computer Vision algorithm developer in Applied Materials and in an Israeli defense company.
Emanuel holds BSc and MSc Degrees in Electrical Engineering, Specializing in statistical signal processing, both from Bar Ilan University.