In this paper, we propose a simple while effective unsupervised deep feature
transfer algorithm for low resolution image classification. No fine-tuning on
convenet filters is required in our method. We use pre-trained convenet to
extract features for both high- and low-resolution images, and then feed them
into a two-layer feature transfer network for knowledge transfer. A SVM
classifier is learned directly using these transferred low resolution features.
Our network can be embedded into the state-of-the-art deep neural networks as a
plug-in feature enhancement module. It preserves data structures in feature
space for high resolution images, and transfers the distinguishing features
from a well-structured source domain (high resolution features space) to a not
well-organized target domain (low resolution features space). Extensive
experiments on VOC2007 test set show that the proposed method achieves
significant improvements over the baseline of using feature extraction.

本文提出了一种简单而有效的无监督深度特征传递算法，用于低分辨率图像分类，无需 fine-tuning。首先，我们使用预先训练的 convenet 提取高分辨率和低分辨率图像的特征，然后将它们馈送到两层特征转移网络以进行知识转移。接着，我们直接使用这些传递的低分辨率特征学习 SVM 分类器。该网络可以作为插件特征增强模块嵌入到最新的深度神经网络中，以保持高分辨率图像特征空间中的数据结构，并将良好结构的源域 (高分辨率特征空间) 的区分特征传递到不良组织的目标域 (低分辨率特征空间) 上。通过对 VOC2007 测试集的大量实验证明，所提出的方法比仅使用特征提取的基线方法实现了显著的性能改善。

用于低分辨率图像分类的无监督深度特征迁移

Unsupervised Deep Feature Transfer for Low Resolution Image  Classification

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。