In the absence of labeled target data, unsupervised domain adaptation
approaches seek to align the marginal distributions of the source and target
domains in order to train a classifier for the target. Unsupervised domain
alignment procedures are category-agnostic and end up misaligning the
categories. We address this problem by deploying a pretrained network to
determine accurate labels for the target domain using a multi-stage
pseudo-label refinement procedure. The filters are based on the confidence,
distance (conformity), and consistency of the pseudo labels. Our results on
multiple datasets demonstrate the effectiveness of our simple procedure in
comparison with complex state-of-the-art techniques.

无监督领域适应的研究中，通过使用预训练网络进行多阶段伪标签优化程序，解决了无标签目标数据中的类别错位问题，证明了该简单方法在多个数据集上比复杂的最先进技术更有效。

使用伪标签进行域自适应

Domain Adaptation Using Pseudo Labels

In deep learning, transferring information from a pretrained network to a
downstream task by finetuning has many benefits. The choice of task head plays
an important role in fine-tuning, as the pretrained and downstream tasks are
usually different. Although there exist many different designs for finetuning,
a full understanding of when and why these algorithms work has been elusive. We
analyze how the choice of task head controls feature adaptation and hence
influences the downstream performance. By decomposing the learning dynamics of
adaptation, we find that the key aspect is the training accuracy and loss at
the beginning of finetuning, which determines the "energy" available for the
feature's adaptation. We identify a significant trend in the effect of changes
in this initial energy on the resulting features after fine-tuning.
Specifically, as the energy increases, the Euclidean and cosine distances
between the resulting and original features increase, while their dot products
(and the resulting features' norm) first increase and then decrease. Inspired
by this, we give several practical principles that lead to better downstream
performance. We analytically prove this trend in an overparamterized linear
setting and verify its applicability to different experimental settings.

通过分析任务头的选择对特征适应的控制，进而影响下游性能，我们发现精细调节一开始的训练准确度和损失，在初始能量的变化对精调后特征的效果有重大影响，最终证明高能量会增加特征的欧几里得和余弦距离，但点积（和精调后特征的范数）会先增加后下降，并提出了一些实际原则来提高下游性能。

如何为微调准备您的任务头

How to prepare your task head for finetuning

The field of neural image compression has witnessed exciting progress as
recently proposed architectures already surpass the established transform
coding based approaches. While, so far, research has mainly focused on
architecture and model improvements, in this work we explore content adaptive
optimization. To this end, we introduce an iterative procedure which adapts the
latent representation to the specific content we wish to compress while keeping
the parameters of the network and the predictive model fixed. Our experiments
show that this allows for an overall increase in rate-distortion performance,
independently of the specific architecture used. Furthermore, we also evaluate
this strategy in the context of adapting a pretrained network to other content
that is different in visual appearance or resolution. Here, our experiments
show that our adaptation strategy can largely close the gap as compared to
models specifically trained for the given content while having the benefit that
no additional data in the form of model parameter updates has to be
transmitted.

本研究探讨了内容自适应优化对神经图像压缩表现的影响，介绍了一种迭代过程，该过程使得潜在表示适应特定内容，同时保持网络和预测模型的参数固定。实验结果表明，与特定结构的模型相比，这种方法能够增加速率 - 失真性能，同时不需要额外传输模型参数更新数据，可用于改进预先训练的网络在不同视觉外观或分辨率的不同内容上的表现。