Inversion methods, such as Textual Inversion, generate personalized images by
incorporating concepts of interest provided by user images. However, existing
methods often suffer from overfitting issues, where the dominant presence of
inverted concepts leads to the absence of other desired concepts. It stems from
the fact that during inversion, the irrelevant semantics in the user images are
also encoded, forcing the inverted concepts to occupy locations far from the
core distribution in the embedding space. To address this issue, we propose a
method that guides the inversion process towards the core distribution for
compositional embeddings. Additionally, we introduce a spatial regularization
approach to balance the attention on the concepts being composed. Our method is
designed as a post-training approach and can be seamlessly integrated with
other inversion methods. Experimental results demonstrate the effectiveness of
our proposed approach in mitigating the overfitting problem and generating more
diverse and balanced compositions of concepts in the synthesized images. The
source code is available at
this https URL

通过引入核心分布和空间正则化方法，我们的研究提出一种针对合成嵌入图像中过拟合问题的反转过程指导方法，使生成的图像中的概念更加多元和均衡。

稳定扩散模型的组合逆转

Compositional Inversion for Stable Diffusion Models

Modern deep neural networks (DNNs) have greatly facilitated the development
of sequential recommender systems by achieving state-of-the-art recommendation
performance on various sequential recommendation tasks. Given a sequence of
interacted items, existing DNN-based sequential recommenders commonly embed
each item into a unique vector to support subsequent computations of the user
interest. However, due to the potentially large number of items, the
over-parameterised item embedding matrix of a sequential recommender has become
a memory bottleneck for efficient deployment in resource-constrained
environments, e.g., smartphones and other edge devices. Furthermore, we observe
that the widely-used multi-head self-attention, though being effective in
modelling sequential dependencies among items, heavily relies on redundant
attention units to fully capture both global and local item-item transition
patterns within a sequence.
In this paper, we introduce a novel lightweight self-attentive network (LSAN)
for sequential recommendation. To aggressively compress the original embedding
matrix, LSAN leverages the notion of compositional embeddings, where each item
embedding is composed by merging a group of selected base embedding vectors
derived from substantially smaller embedding matrices. Meanwhile, to account
for the intrinsic dynamics of each item, we further propose a temporal
context-aware embedding composition scheme. Besides, we develop an innovative
twin-attention network that alleviates the redundancy of the traditional
multi-head self-attention while retaining full capacity for capturing long- and
short-term (i.e., global and local) item dependencies. Comprehensive
experiments demonstrate that LSAN significantly advances the accuracy and
memory efficiency of existing sequential recommenders.

该论文介绍了一种新型轻量级自注意网络 LSAN，通过组成嵌入将原始嵌入矩阵进行压缩并提出了时态上下文感知的嵌入组合方案以解决目前序列推荐中所存在的大量参数嵌入矩阵对资源受限环境的开销过大的问题，并拥有更强的性能表现。

轻量级自注意力序列推荐

Lightweight Self-Attentive Sequential Recommendation

Language models have emerged as a central component across NLP, and a great
deal of progress depends on the ability to cheaply adapt them (e.g., through
finetuning) to new domains and tasks. A language model's vocabulary$-$typically
selected before training and permanently fixed later$-$affects its size and is
part of what makes it resistant to such adaptation. Prior work has used
compositional input embeddings based on surface forms to ameliorate this issue.
In this work, we go one step beyond and propose a fully compositional output
embedding layer for language models, which is further grounded in information
from a structured lexicon (WordNet), namely semantically related words and
free-text definitions. To our knowledge, the result is the first word-level
language model with a size that does not depend on the training vocabulary. We
evaluate the model on conventional language modeling as well as challenging
cross-domain settings with an open vocabulary, finding that it matches or
outperforms previous state-of-the-art output embedding methods and adaptation
approaches. Our analysis attributes the improvements to sample efficiency: our
model is more accurate for low-frequency words.

本研究提出了基于结构化词典的完全组成的输出嵌入层，用于改善语言模型在新域和任务中的适应能力问题，为我们所知，这是第一个单词级别的语言模型，其大小不取决于训练词汇表，并通过低频词的样本效率来提高精度。