Diffusion models have revolutionized the field of image generation, leading
to the proliferation of high-quality models and diverse downstream
applications. However, despite these significant advancements, the current
competitive solutions still suffer from several limitations, including inferior
visual quality, a lack of aesthetic appeal, and inefficient inference, without
a comprehensive solution in sight. To address these challenges, we present
UniFL, a unified framework that leverages feedback learning to enhance
diffusion models comprehensively. UniFL stands out as a universal, effective,
and generalizable solution applicable to various diffusion models, such as
SD1.5 and SDXL. Notably, UniFL incorporates three key components: perceptual
feedback learning, which enhances visual quality; decoupled feedback learning,
which improves aesthetic appeal; and adversarial feedback learning, which
optimizes inference speed. In-depth experiments and extensive user studies
validate the superior performance of our proposed method in enhancing both the
quality of generated models and their acceleration. For instance, UniFL
surpasses ImageReward by 17% user preference in terms of generation quality and
outperforms LCM and SDXL Turbo by 57% and 20% in 4-step inference. Moreover, we
have verified the efficacy of our approach in downstream tasks, including Lora,
ControlNet, and AnimateDiff.

UniFL 是一个统一框架，利用反馈学习全面增强扩散模型，在提升生成模型质量和加速推理方面表现出优越性能。

UniFL：通过统一反馈学习改善稳定扩散

UniFL: Improve Stable Diffusion via Unified Feedback Learning

Human feedback is increasingly used to steer the behaviours of Large Language
Models (LLMs). However, it is unclear how to collect and incorporate feedback
in a way that is efficient, effective and unbiased, especially for highly
subjective human preferences and values. In this paper, we survey existing
approaches for learning from human feedback, drawing on 95 papers primarily
from the ACL and arXiv repositories.First, we summarise the past, pre-LLM
trends for integrating human feedback into language models. Second, we give an
overview of present techniques and practices, as well as the motivations for
using feedback; conceptual frameworks for defining values and preferences; and
how feedback is collected and from whom. Finally, we encourage a better future
of feedback learning in LLMs by raising five unresolved conceptual and
practical challenges.

人类反馈在大型语言模型中被广泛应用，本研究回顾了现有的人类反馈学习方法，并提出了未解决的五个概念和实践上的挑战。

大型语言模型中主观人类偏好和价值的反馈学习的过去、现状和更好未来

The Past, Present and Better Future of Feedback Learning in Large  Language Models for Subjective Human Preferences and Values

Recent research studies revealed that neural networks are vulnerable to
adversarial attacks. State-of-the-art defensive techniques add various
adversarial examples in training to improve models' adversarial robustness.
However, these methods are not universal and can't defend unknown or
non-adversarial evasion attacks. In this paper, we analyze the model robustness
in the decision space. A feedback learning method is then proposed, to
understand how well a model learns and to facilitate the retraining process of
remedying the defects. The evaluations according to a set of distance-based
criteria show that our method can significantly improve models' accuracy and
robustness against different types of evasion attacks. Moreover, we observe the
existence of inter-class inequality and propose to compensate it by changing
the proportions of examples generated in different classes.

通过分析决策空间中的模型鲁棒性，提出一种反馈学习方法，以了解模型的学习情况，促进纠正缺陷的重新训练过程。根据一组基于距离的准则进行的评估表明，我们的方法可以显著提高模型的准确性和对各种逃逸攻击的鲁棒性，同时观察到跨类不平等的存在，并提出通过改变不同类别中生成的示例的比例来弥补它。

神经网络鲁棒性的反馈学习

Feedback Learning for Improving the Robustness of Neural Networks

During the past few decades, knowledge bases (KBs) have experienced rapid
growth. Nevertheless, most KBs still suffer from serious incompletion.
Researchers proposed many tasks such as knowledge base completion and relation
prediction to help build the representation of KBs. However, there are some
issues unsettled towards enriching the KBs. Knowledge base completion and
relation prediction assume that we know two elements of the fact triples and we
are going to predict the missing one. This assumption is too restricted in
practice and prevents it from discovering new facts directly. To address this
issue, we propose a new task, namely, fact discovery from knowledge base. This
task only requires that we know the head entity and the goal is to discover
facts associated with the head entity. To tackle this new problem, we propose a
novel framework that decomposes the discovery problem into several facet
discovery components. We also propose a novel auto-encoder based facet
component to estimate some facets of the fact. Besides, we propose a feedback
learning component to share the information between each facet. We evaluate our
framework using a benchmark dataset and the experimental results show that our
framework achieves promising results. We also conduct extensive analysis of our
framework in discovering different kinds of facts. The source code of this
paper can be obtained from this https URL

提出了一种新的知识库任务，即从头实体中发现相关事实的问题，并提出了一个新的框架来解决该问题，其中使用自编码器组件和反馈学习组件来实现。实验结果表明，该框架取得了有希望的结果。