Self-supervision has emerged as a propitious method for visual representation
learning after the recent paradigm shift from handcrafted pretext tasks to
instance-similarity based approaches. Most state-of-the-art methods enforce
similarity between various augmentations of a given image, while some methods
additionally use contrastive approaches to explicitly ensure diverse
representations. While these approaches have indeed shown promising direction,
they require a significantly larger number of training iterations when compared
to the supervised counterparts. In this work, we explore reasons for the slow
convergence of these methods, and further propose to strengthen them using
well-posed auxiliary tasks that converge significantly faster, and are also
useful for representation learning. The proposed method utilizes the task of
rotation prediction to improve the efficiency of existing state-of-the-art
methods. We demonstrate significant gains in performance using the proposed
method on multiple datasets, specifically for lower training epochs.

本研究提出一种新的辅助任务 —— 旋转预测，用于加强现有最先进的基于相似性和对比性方法，并在多个数据集上证明了我们的方法能显着提高性能。

高效有效的自监督视觉表示学习

Towards Efficient and Effective Self-Supervised Learning of Visual  Representations

We propose a new policy parameterization for representing 3D rotations during
reinforcement learning. Today in the continuous control reinforcement learning
literature, many stochastic policy parameterizations are Gaussian. We argue
that universally applying a Gaussian policy parameterization is not always
desirable for all environments. One such case in particular where this is true
are tasks that involve predicting a 3D rotation output, either in isolation, or
coupled with translation as part of a full 6D pose output. Our proposed Bingham
Policy Parameterization (BPP) models the Bingham distribution and allows for
better rotation (quaternion) prediction over a Gaussian policy parameterization
in a range of reinforcement learning tasks. We evaluate BPP on the rotation
Wahba problem task, as well as a set of vision-based next-best pose robot
manipulation tasks from RLBench. We hope that this paper encourages more
research into developing other policy parameterization that are more suited for
particular environments, rather than always assuming Gaussian.

提出了一种新的策略参数化方式，Bingham Policy Parameterization（BPP），它可以更好地模拟 Bingham 分布，从而比高斯策略参数化在一系列强化学习任务中具有更好的旋转（四元数）预测能力。

强化学习中三维旋转的 Bingham 策略参数化

Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning

Understanding classifier decision under novel environments is central to the
community, and a common practice is evaluating it on labeled test sets.
However, in real-world testing, image annotations are difficult and expensive
to obtain, especially when the test environment is changing. A natural question
then arises: given a trained classifier, can we evaluate its accuracy on
varying unlabeled test sets? In this work, we train semantic classification and
rotation prediction in a multi-task way. On a series of datasets, we report an
interesting finding, i.e., the semantic classification accuracy exhibits a
strong linear relationship with the accuracy of the rotation prediction task
(Pearson's Correlation r > 0.88). This finding allows us to utilize linear
regression to estimate classifier performance from the accuracy of rotation
prediction which can be obtained on the test set through the freely generated
rotation labels.

本文针对图像分类器在新环境下的分类决策问题，通过多任务学习方法，发现语义分类精度与旋转预测任务准确性有强线性关系（Pearson 相关系数 r> 0.88），从旋转预测任务的准确性，应用线性回归估计分类器性能。