  Fine-grained vehicle recognition (FGVR) is an essential fundamental
technology for intelligent transportation systems, but very difficult because
of its inherent intra-class variation. Most previous FGVR studies only focus on
the intra-class variation caused by different shooting angles, positions, etc.,
while the intra-class variation caused by image noise has received little
attention. This paper proposes a progressive multi-task anti-noise learning
(PMAL) framework and a progressive multi-task distilling (PMD) framework to
solve the intra-class variation problem in FGVR due to image noise. The PMAL
framework achieves high recognition accuracy by treating image denoising as an
additional task in image recognition and progressively forcing a model to learn
noise invariance. The PMD framework transfers the knowledge of the PMAL-trained
model into the original backbone network, which produces a model with about the
same recognition accuracy as the PMAL-trained model, but without any additional
overheads over the original backbone network. Combining the two frameworks, we
obtain models that significantly exceed previous state-of-the-art methods in
recognition accuracy on two widely-used, standard FGVR datasets, namely
Stanford Cars, and CompCars, as well as three additional surveillance
image-based vehicle-type classification datasets, namely Beijing Institute of
Technology (BIT)-Vehicle, Vehicle Type Image Data 2 (VTID2), and Vehicle Images
Dataset for Make Model Recognition (VIDMMR), without any additional overheads
over the original backbone networks. The source code is available at
https://github.com/Dichao-Liu/Anti-noise_FGVR
