There is a growing interest in cost-effective self-supervised fine-tuning
(SSFT) of self-supervised learning (SSL)-based speech models to obtain
task-specific representations. These task-specific representations are used for
robust performance on various downstream tasks by fine-tuning on the labelled
data. This work presents a cost-effective SSFT method named Self-supervised
Correspondence (SCORE) fine-tuning to adapt the SSL speech representations for
content-related tasks. The proposed method uses a correspondence training
strategy, aiming to learn similar representations from perturbed speech and
original speech. Commonly used data augmentation techniques for content-related
tasks (ASR) are applied to obtain perturbed speech. SCORE fine-tuned HuBERT
outperforms the vanilla HuBERT on SUPERB benchmark with only a few hours of
fine-tuning (< 5 hrs) on a single GPU for automatic speech recognition, phoneme
recognition, and query-by-example tasks, with relative improvements of 1.09%,
3.58%, and 12.65%, respectively. SCORE provides competitive results with the
recently proposed SSFT method SPIN, using only 1/3 of the processed speech
compared to SPIN.

提出了一种称为 SCORE 微调的自我监督一致性微调方法，用于自适应自我监督学习语音表示，以适应与内容相关的任务。与 SPIN 方法相比，SCORE 仅使用 1/3 的处理语音，在超级基准（SUPERB benchmark）上表现出优越的结果。(Translated simplified Chinese summary)

SCORE：自监督对应微调以改善内容表达

SCORE: Self-supervised Correspondence Fine-tuning for Improved Content  Representations

The Euler Characteristic Transform (ECT) has proven to be a powerful
representation, combining geometrical and topological characteristics of shapes
and graphs. However, the ECT was hitherto unable to learn task-specific
representations. We overcome this issue and develop a novel computational layer
that enables learning the ECT in an end-to-end fashion. Our method DECT is fast
and computationally efficient, while exhibiting performance on a par with more
complex models in both graph and point cloud classification tasks. Moreover, we
show that this seemingly unexpressive statistic still provides the same
topological expressivity as more complex topological deep learning layers
provide.

我们开发了一种新的计算层 DECT，它能够以端到端的方式学习 Euler Characteristic Transform，并在图形和点云分类任务中展现出与复杂模型相当的性能；此外，我们还发现这种看似非表达性的统计量仍然具有与更复杂的拓扑深度学习层一样的拓扑表达能力。

可微分的欧拉特征变换用于形状分类

Differentiable Euler Characteristic Transforms for Shape Classification

We propose VRL3, a powerful data-driven framework with a simple design for
solving challenging visual deep reinforcement learning (DRL) tasks. We analyze
a number of major obstacles in taking a data-driven approach, and present a
suite of design principles, novel findings, and critical insights about
data-driven visual DRL. Our framework has three stages: in stage 1, we leverage
non-RL datasets (e.g. ImageNet) to learn task-agnostic visual representations;
in stage 2, we use offline RL data (e.g. a limited number of expert
demonstrations) to convert the task-agnostic representations into more powerful
task-specific representations; in stage 3, we fine-tune the agent with online
RL. On a set of challenging hand manipulation tasks with sparse reward and
realistic visual inputs, compared to the previous SOTA, VRL3 achieves an
average of 780% better sample efficiency. And on the hardest task, VRL3 is
1220% more sample efficient (2440% when using a wider encoder) and solves the
task with only 10% of the computation. These significant results clearly
demonstrate the great potential of data-driven deep reinforcement learning.

提出了 VRL3 这一数据驱动框架，在视觉深度强化学习中取得了极高的效率，并且在手部操作任务中的样本效率比先前最先进的方法高出了 780％。VRL3 在最困难的任务中样本效率提高了 1220％（使用更宽的编码器达到 2440％）并且只需 10％的计算量就能解决该任务，这些结果清楚地证明了数据驱动深度强化学习的巨大潜力。