Unsupervised object detection using deep neural networks is typically a
difficult problem with few to no guarantees about the learned representation.
In this work we present the first unsupervised object detection method that is
theoretically guaranteed to recover the true object positions up to
quantifiable small shifts. We develop an unsupervised object detection
architecture and prove that the learned variables correspond to the true object
positions up to small shifts related to the encoder and decoder receptive field
sizes, the object sizes, and the widths of the Gaussians used in the rendering
process. We perform detailed analysis of how the error depends on each of these
variables and perform synthetic experiments validating our theoretical
predictions up to a precision of individual pixels. We also perform experiments
on CLEVR-based data and show that, unlike current SOTA object detection methods
(SAM, CutLER), our method's prediction errors always lie within our theoretical
bounds. We hope that this work helps open up an avenue of research into object
detection methods with theoretical guarantees.

通过深度神经网络实现无监督目标检测是一个通常具有少量或没有有关学习表示的保证的难题。我们提出了第一个在理论上保证能够恢复真实目标位置的无监督目标检测方法，同时与编码器和解码器的感受野尺寸、目标尺寸和渲染过程中使用的高斯宽度相关的小偏移有关。我们对这些变量如何影响误差进行了详细分析，并进行了合成实验证实了我们的理论预测，像素级精度。我们还在基于 CLEVR 的数据上进行实验，结果表明，与当前 SOTA 目标检测方法（SAM，CutLER）不同，我们的方法的预测误差始终在我们的理论界限内。我们希望这项工作能够为具有理论保证的目标检测方法开辟一条研究途径。

具有理论保证的无监督目标检测

Unsupervised Object Detection with Theoretical Guarantees

Fine-tuning a pre-trained model (such as BERT, ALBERT, RoBERTa, T5, GPT,
etc.) has proven to be one of the most promising paradigms in recent NLP
research. However, numerous recent works indicate that fine-tuning suffers from
the instability problem, i.e., tuning the same model under the same setting
results in significantly different performance. Many recent works have proposed
different methods to solve this problem, but there is no theoretical
understanding of why and how these methods work. In this paper, we propose a
novel theoretical stability analysis of fine-tuning that focuses on two
commonly used settings, namely, full fine-tuning and head tuning. We define the
stability under each setting and prove the corresponding stability bounds. The
theoretical bounds explain why and how several existing methods can stabilize
the fine-tuning procedure. In addition to being able to explain most of the
observed empirical discoveries, our proposed theoretical analysis framework can
also help in the design of effective and provable methods. Based on our theory,
we propose three novel strategies to stabilize the fine-tuning procedure,
namely, Maximal Margin Regularizer (MMR), Multi-Head Loss (MHLoss), and Self
Unsupervised Re-Training (SURT). We extensively evaluate our proposed
approaches on 11 widely used real-world benchmark datasets, as well as hundreds
of synthetic classification datasets. The experiment results show that our
proposed methods significantly stabilize the fine-tuning procedure and also
corroborate our theoretical analysis.

通过理论稳定性分析和提出的新策略，解决预训练模型微调在同样设置下性能稳定性差异问题。

微调预训练模型的稳定性分析

A Stability Analysis of Fine-Tuning a Pre-Trained Model

Over the last decade, research on automated parameter tuning, often referred
to as automatic algorithm configuration (AAC), has made significant progress.
Although the usefulness of such tools has been widely recognized in real world
applications, the theoretical foundations of AAC are still very weak. This
paper addresses this gap by studying the performance estimation problem in AAC.
More specifically, this paper first proves the universal best performance
estimator in a practical setting, and then establishes theoretical bounds on
the estimation error, i.e., the difference between the training performance and
the true performance for a parameter configuration, considering finite and
infinite configuration spaces respectively. These findings were verified in
extensive experiments conducted on four algorithm configuration scenarios
involving different problem domains. Moreover, insights for enhancing existing
AAC methods are also identified.

本文探讨了自动算法配置 (AAC) 中的性能估计问题，首先证明了在实际设置中的通用最佳性能估计器，然后分别考虑了有限和无限配置空间，建立了估计误差的理论界限，并在四个涉及不同问题领域的算法配置情景中进行了广泛的实验验证。此外，本文还提出了加强现有 AAC 方法的见解。