Can we take a recurrent neural network (RNN) trained to translate between
languages and augment it to support a new natural language without retraining
the model from scratch? Can we fix the faulty behavior of the RNN by replacing
portions associated with the faulty behavior? Recent works on decomposing a
fully connected neural network (FCNN) and convolutional neural network (CNN)
into modules have shown the value of engineering deep models in this manner,
which is standard in traditional SE but foreign for deep learning models.
However, prior works focus on the image-based multiclass classification
problems and cannot be applied to RNN due to (a) different layer structures,
(b) loop structures, (c) different types of input-output architectures, and (d)
usage of both nonlinear and logistic activation functions. In this work, we
propose the first approach to decompose an RNN into modules. We study different
types of RNNs, i.e., Vanilla, LSTM, and GRU. Further, we show how such RNN
modules can be reused and replaced in various scenarios. We evaluate our
approach against 5 canonical datasets (i.e., Math QA, Brown Corpus,
Wiki-toxicity, Clinc OOS, and Tatoeba) and 4 model variants for each dataset.
We found that decomposing a trained model has a small cost (Accuracy: -0.6%,
BLEU score: +0.10%). Also, the decomposed modules can be reused and replaced
without needing to retrain.

本文提出了一种将循环神经网络分解为模块化的方法，可以解决现有神经网络模型的缺陷，并且可以在不需要从头开始训练模型的情况下重用和替换这些模块，该方法通过 5 个标准数据集的评估发现，其准确率的下降仅为 0.6％而 BLEU 得分上升了 0.10％。

将递归神经网络分解为模块以实现可重用性和替代性

Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement

Learning models that offer robust out-of-distribution generalization and fast
adaptation is a key challenge in modern machine learning. Modelling causal
structure into neural networks holds the promise to accomplish robust zero and
few-shot adaptation. Recent advances in differentiable causal discovery have
proposed to factorize the data generating process into a set of modules, i.e.
one module for the conditional distribution of every variable where only causal
parents are used as predictors. Such a modular decomposition of knowledge
enables adaptation to distributions shifts by only updating a subset of
parameters. In this work, we systematically study the generalization and
adaption performance of such modular neural causal models by comparing it to
monolithic models and structured models where the set of predictors is not
constrained to causal parents. Our analysis shows that the modular neural
causal models outperform other models on both zero and few-shot adaptation in
low data regimes and offer robust generalization. We also found that the
effects are more significant for sparser graphs as compared to denser graphs.

本文研究了神经因果模型在零样本和小样本情况下的性能，并将其与单体模型和结构模型进行比较，其中预测变量的集合没有被限制为因果父节点。研究发现，相对于其他模型，因果神经模型在低数据量情况下具有更好的零样本和小样本适应性，并提供了稳健的泛化能力。此外，研究发现，比起更密集的图，对于更稀疏的图，这种效果更为显著。

因果模型的泛化和适应性能

On the Generalization and Adaption Performance of Causal Models

In this paper, we are interested in editing text in natural images, which
aims to replace or modify a word in the source image with another one while
maintaining its realistic look. This task is challenging, as the styles of both
background and text need to be preserved so that the edited image is visually
indistinguishable from the source image. Specifically, we propose an end-to-end
trainable style retention network (SRNet) that consists of three modules: text
conversion module, background inpainting module and fusion module. The text
conversion module changes the text content of the source image into the target
text while keeping the original text style. The background inpainting module
erases the original text, and fills the text region with appropriate texture.
The fusion module combines the information from the two former modules, and
generates the edited text images. To our knowledge, this work is the first
attempt to edit text in natural images at the word level. Both visual effects
and quantitative results on synthetic and real-world dataset (ICDAR 2013) fully
confirm the importance and necessity of modular decomposition. We also conduct
extensive experiments to validate the usefulness of our method in various
real-world applications such as text image synthesis, augmented reality (AR)
translation, information hiding, etc.

本文针对自然图像中的文本编辑问题，提出了一个端到端的训练风格保留网络（SRNet）来实现文本内容的替换和修改，实验证明与其他方法相比，该方法在视觉效果和定量结果上表现良好。