Learning-based Text-to-Image (TTI) models like Stable Diffusion have
revolutionized the way visual content is generated in various domains. However,
recent research has shown that nonnegligible social bias exists in current
state-of-the-art TTI systems, which raises important concerns. In this work, we
target resolving the social bias in TTI diffusion models. We begin by
formalizing the problem setting and use the text descriptions of bias groups to
establish an unsafe direction for guiding the diffusion process. Next, we
simplify the problem into a weight optimization problem and attempt a
Reinforcement solver, Policy Gradient, which shows sub-optimal performance with
slow convergence. Further, to overcome limitations, we propose an iterative
distribution alignment (IDA) method. Despite its simplicity, we show that IDA
shows efficiency and fast convergence in resolving the social bias in TTI
diffusion models. Our code will be released.

目前的学习型文本到图像模型存在社会偏见问题，本研究提出了一种基于迭代分布对齐的方法，通过权重优化来解决社会偏见问题。

消除文本到图像扩散模型的偏见

Debiasing Text-to-Image Diffusion Models

This work addresses weight optimization problem for fully-connected
feed-forward neural networks. Unlike existing approaches that are based on
back-propagation (BP) and chain rule gradient-based optimization (which implies
iterative execution, potentially burdensome and time-consuming in some cases),
the proposed approach offers the solution for weight optimization in
closed-form by means of least squares (LS) methodology. In the case where the
input-to-output mapping is injective, the new approach optimizes the weights in
a back-propagating fashion in a single iteration by jointly optimizing a set of
weights in each layer for each neuron. In the case where the input-to-output
mapping is not injective (e.g., in classification problems), the proposed
solution is easily adapted to obtain its final solution in a few iterations. An
important advantage over the existing solutions is that these computations (for
all neurons in a layer) are independent from each other; thus, they can be
carried out in parallel to optimize all weights in a given layer
simultaneously. Furthermore, its running time is deterministic in the sense
that one can obtain the exact number of computations necessary to optimize the
weights in all network layers (per iteration, in the case of non-injective
mapping). Our simulation and empirical results show that the proposed scheme,
BPLS, works well and is competitive with existing ones in terms of accuracy,
but significantly surpasses them in terms of running time. To summarize, the
new method is straightforward to implement, is competitive and computationally
more efficient than the existing ones, and is well-tailored for parallel
implementation.

该研究针对全连接前馈神经网络的权重优化问题，提出了使用最小二乘法来实现权重优化的方法。这种方法具有闭合形式解，能够在单次迭代中通过反向传播的方式优化每层神经元的权重，并且能够在几次迭代中适应输入到输出映射不是单射的情况。与现有解决方案相比，这种方法的计算是并行的，具有确定性的运行时间，并且在准确性方面与现有解决方案竞争，但在运行时间方面明显优于它们。总之，这种新方法实现简单，与现有方法相比在计算效率上更高，并且适于并行实现。

全连接前馈神经网络中权重优化的闭式解决方案

A Closed-form Solution for Weight Optimization in Fully-connected  Feed-forward Neural Networks

In contrast to traditional weight optimization in a continuous space, we
demonstrate the existence of effective random networks whose weights are never
updated. By selecting a weight among a fixed set of random values for each
individual connection, our method uncovers combinations of random weights that
match the performance of traditionally-trained networks of the same capacity.
We refer to our networks as "slot machines" where each reel (connection)
contains a fixed set of symbols (random values). Our backpropagation algorithm
"spins" the reels to seek "winning" combinations, i.e., selections of random
weight values that minimize the given loss. Quite surprisingly, we find that
allocating just a few random values to each connection (e.g., 8 values per
connection) yields highly competitive combinations despite being dramatically
more constrained compared to traditionally learned weights. Moreover,
finetuning these combinations often improves performance over the trained
baselines. A randomly initialized VGG-19 with 8 values per connection contains
a combination that achieves 91% test accuracy on CIFAR-10. Our method also
achieves an impressive performance of 98.2% on MNIST for neural networks
containing only random weights.

提出了一种称为 “老虎机” 的新型神经网络，其中每个连接包含一组随机值，使用反向传播算法来寻求最小化给定损失的随机权重值的组合，证明分配给每个连接仅 8 个随机值就能达到高竞争力的组合，即使与传统的学习权重相比高度受限的情况下，也通常可以改进基线训练结果。