Vision transformers have revolutionized computer vision, but their
computational demands present challenges for training and deployment. This
paper introduces LOTUS (LOttery Transformers with Ultra Sparsity), a novel
method that leverages data lottery ticket selection and sparsity pruning to
accelerate vision transformer training while maintaining accuracy. Our approach
focuses on identifying and utilizing the most informative data subsets and
eliminating redundant model parameters to optimize the training process.
Through extensive experiments, we demonstrate the effectiveness of LOTUS in
achieving rapid convergence and high accuracy with significantly reduced
computational requirements. This work highlights the potential of combining
data selection and sparsity techniques for efficient vision transformer
training, opening doors for further research and development in this area.

LOTUS 是一种利用数据抽奖选择和稀疏剪枝来加速视觉变换器训练的新方法，通过识别和利用最信息丰富的数据子集并消除冗余模型参数来优化训练过程，通过广泛的实验证明了 LOTUS 在快速收敛和高准确度达到显著降低计算要求方面的有效性，这项工作突出了在高效视觉变换器训练方面结合数据选择和稀疏技术的潜力，为进一步的研究和开发提供了机会。

LOTUS: 通过稀疏修剪和数据抽奖提高 Transformer 效率

LOTUS: Improving Transformer Efficiency with Sparsity Pruning and Data  Lottery Tickets

Backdoor attack poses a significant security threat to Deep Learning
applications. Existing attacks are often not evasive to established backdoor
detection techniques. This susceptibility primarily stems from the fact that
these attacks typically leverage a universal trigger pattern or transformation
function, such that the trigger can cause misclassification for any input. In
response to this, recent papers have introduced attacks using sample-specific
invisible triggers crafted through special transformation functions. While
these approaches manage to evade detection to some extent, they reveal
vulnerability to existing backdoor mitigation techniques. To address and
enhance both evasiveness and resilience, we introduce a novel backdoor attack
LOTUS. Specifically, it leverages a secret function to separate samples in the
victim class into a set of partitions and applies unique triggers to different
partitions. Furthermore, LOTUS incorporates an effective trigger focusing
mechanism, ensuring only the trigger corresponding to the partition can induce
the backdoor behavior. Extensive experimental results show that LOTUS can
achieve high attack success rate across 4 datasets and 7 model structures, and
effectively evading 13 backdoor detection and mitigation techniques. The code
is available at this https URL

通过引入一种名为 LOTUS 的新型后门攻击方法，该方法利用秘密函数将受害类别样本分成一组分区，并对不同的分区应用唯一的触发器，从而在攻击中提高了逃避检测和抵御能力。扩展实验结果显示，LOTUS 能够在 4 个数据集和 7 个模型结构上实现高攻击成功率，并有效规避 13 种后门检测和抵御技术。

LOTUS: 透过子分区实现隐蔽和弹性的后门攻击

LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning

We introduce LOTUS, a continual imitation learning algorithm that empowers a
physical robot to continuously and efficiently learn to solve new manipulation
tasks throughout its lifespan. The core idea behind LOTUS is constructing an
ever-growing skill library from a sequence of new tasks with a small number of
human demonstrations. LOTUS starts with a continual skill discovery process
using an open-vocabulary vision model, which extracts skills as recurring
patterns presented in unsegmented demonstrations. Continual skill discovery
updates existing skills to avoid catastrophic forgetting of previous tasks and
adds new skills to solve novel tasks. LOTUS trains a meta-controller that
flexibly composes various skills to tackle vision-based manipulation tasks in
the lifelong learning process. Our comprehensive experiments show that LOTUS
outperforms state-of-the-art baselines by over 11% in success rate, showing its
superior knowledge transfer ability compared to prior methods. More results and
videos can be found on the project website:
this https URL

LOTUS 是一种持续模仿学习算法，使物理机器人能够在其寿命期内持续而高效地学习解决新的操作任务。通过使用一个开放词汇表视觉模型进行持续的技能发现过程，LOTUS 能构建一个不断增长的技能库，从一系列以少量人类示范展示的新任务中提取技能，并通过训练一个元控制器灵活地组合各种技能来解决基于视觉的操作任务，显示出比先前方法更强的知识传递能力。