The problem of data augmentation in feature space is considered. A new
architecture, denoted the FeATure TransfEr Network (FATTEN), is proposed for
the modeling of feature trajectories induced by variations of object pose. This
architecture exploits a parametrization of the pose manifold in terms of pose
and appearance. This leads to a deep encoder/decoder network architecture,
where the encoder factors into an appearance and a pose predictor. Unlike
previous attempts at trajectory transfer, FATTEN can be efficiently trained
end-to-end, with no need to train separate feature transfer functions. This is
realized by supplying the decoder with information about a target pose and the
use of a multi-task loss that penalizes category- and pose-mismatches. In
result, FATTEN discourages discontinuous or non-smooth trajectories that fail
to capture the structure of the pose manifold, and generalizes well on object
recognition tasks involving large pose variation. Experimental results on the
artificial ModelNet database show that it can successfully learn to map source
features to target features of a desired pose, while preserving class identity.
Most notably, by using feature space transfer for data augmentation (w.r.t.
pose and depth) on SUN-RGBD objects, we demonstrate considerable performance
improvements on one/few-shot object recognition in a transfer learning setup,
compared to current state-of-the-art methods.

该论文提出了一种新的体系结构 —— 特征转移网络（FATTEN），用于建模由目标姿态变化引起的特征轨迹的数据增强方法，并且该方法在目标识别任务中表现出了良好的泛化效果。