  Long-tailed image recognition presents massive challenges to deep learning
systems since the imbalance between majority (head) classes and minority (tail)
classes severely skews the data-driven deep neural networks. Previous methods
tackle with data imbalance from the viewpoints of data distribution, feature
space, and model design, etc. In this work, instead of directly learning a
recognition model, we suggest confronting the bottleneck of head-to-tail bias
before classifier learning, from the previously omitted perspective of
balancing label space. To alleviate the head-to-tail bias, we propose a concise
paradigm by progressively adjusting label space and dividing the head classes
and tail classes, dynamically constructing balance from imbalance to facilitate
the classification. With flexible data filtering and label space mapping, we
can easily embed our approach to most classification models, especially the
decoupled training methods. Besides, we find the separability of head-tail
classes varies among different features with different inductive biases. Hence,
our proposed model also provides a feature evaluation method and paves the way
for long-tailed feature learning. Extensive experiments show that our method
can boost the performance of state-of-the-arts of different types on
widely-used benchmarks. Code is available at https://github.com/silicx/DLSA.
