To better understand the behavior of image classifiers, it is useful to
visualize the contribution of individual pixels to the model prediction. In
this study, we propose a method, MoXI~($\textbf{Mo}$del e$\textbf{X}$planation
by $\textbf{I}$nteractions), that efficiently and accurately identifies a group
of pixels with high prediction confidence. The proposed method employs
game-theoretic concepts, Shapley values and interactions, taking into account
the effects of individual pixels and the cooperative influence of pixels on
model confidence. Theoretical analysis and experiments demonstrate that our
method better identifies the pixels that are highly contributing to the model
outputs than widely-used visualization methods using Grad-CAM, Attention
rollout, and Shapley value. While prior studies have suffered from the
exponential computational cost in the computation of Shapley value and
interactions, we show that this can be reduced to linear cost for our task.

为了更好地理解图像分类器的行为，本研究提出了一种方法 MoXI（模型交互解释），该方法通过有效且准确地识别一组具有高预测置信度的像素，采用了博弈论的概念、Shapley 值和像素之间的相互影响，既考虑了个别像素的影响，又考虑了像素对模型置信度的合作影响。理论分析和实验证明了我们的方法相比使用 Grad-CAM、Attention 展开和 Shapley 值的广泛使用的可视化方法更好地识别出对模型输出有高度贡献的像素。而之前的研究在计算 Shapley 值和交互作用时遇到了指数级计算成本的问题，我们在本研究中证明了这可以降低到线性成本。

使用相互作用识别重要像素组

Identifying Important Group of Pixels using Interactions

Intelligent agents need to select long sequences of actions to solve complex
tasks. While humans easily break down tasks into subgoals and reach them
through millions of muscle commands, current artificial intelligence is limited
to tasks with horizons of a few hundred decisions, despite large compute
budgets. Research on hierarchical reinforcement learning aims to overcome this
limitation but has proven to be challenging, current methods rely on manually
specified goal spaces or subtasks, and no general solution exists. We introduce
Director, a practical method for learning hierarchical behaviors directly from
pixels by planning inside the latent space of a learned world model. The
high-level policy maximizes task and exploration rewards by selecting latent
goals and the low-level policy learns to achieve the goals. Despite operating
in latent space, the decisions are interpretable because the world model can
decode goals into images for visualization. Director outperforms exploration
methods on tasks with sparse rewards, including 3D maze traversal with a
quadruped robot from an egocentric camera and proprioception, without access to
the global position or top-down view that was used by prior work. Director also
learns successful behaviors across a wide range of environments, including
visual control, Atari games, and DMLab levels.

通过在一个学习好的世界模型的潜在空间 planning，我们引入了一种名叫 Director 的实用方法，直接从像素学习分层行为，无需手动指定目标空间或子任务，并在具有稀疏奖励的任务中表现超越了探索方法。

基于像素的深度分层规划

Deep Hierarchical Planning from Pixels

We propose a simple data augmentation technique that can be applied to
standard model-free reinforcement learning algorithms, enabling robust learning
directly from pixels without the need for auxiliary losses or pre-training. The
approach leverages input perturbations commonly used in computer vision tasks
to regularize the value function. Existing model-free approaches, such as Soft
Actor-Critic (SAC), are not able to train deep networks effectively from image
pixels. However, the addition of our augmentation method dramatically improves
SAC's performance, enabling it to reach state-of-the-art performance on the
DeepMind control suite, surpassing model-based (Dreamer, PlaNet, and SLAC)
methods and recently proposed contrastive learning (CURL). Our approach can be
combined with any model-free reinforcement learning algorithm, requiring only
minor modifications. An implementation can be found at
this https URL

该研究提出了一项简单的数据增强技术，可以应用于标准的无模型强化学习算法，从像素直接学习，无需辅助损失或预训练，并使用图像处理来规范化值函数，实现与 DeepMind 控制套件相关的现有模型的最高表现。