This work aims to reproduce results from the CVPR 2020 paper by Gidaris et
al. Self-supervised learning (SSL) is used to learn feature representations of
an image using an unlabeled dataset. This work proposes to use bag-of-words
(BoW) deep feature descriptors as a self-supervised learning target to learn
robust, deep representations. BowNet is trained to reconstruct the histogram of
visual words (ie. the deep BoW descriptor) of a reference image when presented
a perturbed version of the image as input. Thus, this method aims to learn
perturbation-invariant and context-aware image features that can be useful for
few-shot tasks or supervised downstream tasks. In the paper, the author
describes BowNet as a network consisting of a convolutional feature extractor
$\Phi(\cdot)$ and a Dense-softmax layer $\Omega(\cdot)$ trained to predict BoW
features from images. After BoW training, the features of $\Phi$ are used in
downstream tasks. For this challenge we were trying to build and train a
network that could reproduce the CIFAR-100 accuracy improvements reported in
the original paper. However, we were unsuccessful in reproducing an accuracy
improvement comparable to what the authors mentioned. This could be for a
variety of factors and we believe that time constraints were the primary
bottleneck.

该研究使用无标签数据自我监督学习的方法，使用词袋深度描述符作为学习目标以学习鲁棒、深度特征，该方法可以学习具有扰动不变性和上下文感知的图像特征，适用于几乎无样本的任务或有监督的后续任务，其中将深度特征用于 CIFAR-100 分类任务并未能像原论文中所述显著提高分类准确性。