Class-incremental learning (CIL) is a challenging task that involves
continually learning to categorize classes into new tasks without forgetting
previously learned information. The advent of the large pre-trained models
(PTMs) has fast-tracked the progress in CIL due to the highly transferable PTM
representations, where tuning a small set of parameters results in
state-of-the-art performance when compared with the traditional CIL methods
that are trained from scratch. However, repeated fine-tuning on each task
destroys the rich representations of the PTMs and further leads to forgetting
previous tasks. To strike a balance between the stability and plasticity of
PTMs for CIL, we propose a novel perspective of eliminating training on every
new task and instead performing test-time adaptation (TTA) directly on the test
instances. Concretely, we propose "Test-Time Adaptation for Class-Incremental
Learning" (TTACIL) that first fine-tunes Layer Norm parameters of the PTM on
each test instance for learning task-specific features, and then resets them
back to the base model to preserve stability. As a consequence, TTACIL does not
undergo any forgetting, while benefiting each task with the rich PTM features.
Additionally, by design, our method is robust to common data corruptions. Our
TTACIL outperforms several state-of-the-art CIL methods when evaluated on
multiple CIL benchmarks under both clean and corrupted data.

针对类增量学习问题，我们提出了一种基于测试时间适应的方法（TTACIL），通过在每个测试实例上对预训练模型的层归一化参数进行微调，以学习任务特定特征，并将其重置为基本模型以保持稳定性，从而避免了遗忘，并在干净和损坏的数据下的多个类增量学习基准测试中胜过了其他最先进的方法。

借助测试时间适应，在大型预训练模型时代重新思考类别增量学习

Rethinking Class-incremental Learning in the Era of Large Pre-trained  Models via Test-Time Adaptation

Developing and training deep learning models is expensive, so software
engineers have begun to reuse pre-trained deep learning models (PTMs) and
fine-tune them for downstream tasks. Despite the wide-spread use of PTMs, we
know little about the corresponding software engineering behaviors and
challenges.
To enable the study of software engineering with PTMs, we present the
PeaTMOSS dataset: Pre-Trained Models in Open-Source Software. PeaTMOSS has
three parts: a snapshot of (1) 281,638 PTMs, (2) 27,270 open-source software
repositories that use PTMs, and (3) a mapping between PTMs and the projects
that use them. We challenge PeaTMOSS miners to discover software engineering
practices around PTMs. A demo and link to the full dataset are available at:
this https URL

使用 PeaTMOSS 数据集，研究了深度学习模型在软件工程中的应用和挑战，该数据集包含大量预训练模型、开源软件仓库以及二者之间的映射。

PeaTMOSS：开源软件中挖掘预训练模型

PeaTMOSS: Mining Pre-Trained Models in Open-Source Software

Protein-protein interactions (PPIs) are critical to normal cellular function
and are related to many disease pathways. However, only 4% of PPIs are
annotated with PTMs in biological knowledge databases such as IntAct, mainly
performed through manual curation, which is neither time nor cost-effective. We
use the IntAct PPI database to create a distant supervised dataset annotated
with interacting protein pairs, their corresponding PTM type, and associated
abstracts from the PubMed database. We train an ensemble of BioBERT models -
dubbed PPI-BioBERT-x10 to improve confidence calibration. We extend the use of
ensemble average confidence approach with confidence variation to counteract
the effects of class imbalance to extract high confidence predictions. The
PPI-BioBERT-x10 model evaluated on the test set resulted in a modest F1-micro
41.3 (P =5 8.1, R = 32.1). However, by combining high confidence and low
variation to identify high quality predictions, tuning the predictions for
precision, we retained 19% of the test predictions with 100% precision. We
evaluated PPI-BioBERT-x10 on 18 million PubMed abstracts and extracted 1.6
million (546507 unique PTM-PPI triplets) PTM-PPI predictions, and filter ~ 5700
(4584 unique) high confidence predictions. Of the 5700, human evaluation on a
small randomly sampled subset shows that the precision drops to 33.7% despite
confidence calibration and highlights the challenges of generalisability beyond
the test set even with confidence calibration. We circumvent the problem by
only including predictions associated with multiple papers, improving the
precision to 58.8%. In this work, we highlight the benefits and challenges of
deep learning-based text mining in practice, and the need for increased
emphasis on confidence calibration to facilitate human curation efforts.

在这项研究中，作者使用 IntAct PPI 数据库创建了一个远程监督的数据集，用 PPI-BioBERT-x10 训练集成的 BioBERT 模型来预测蛋白质对之间的 PTMs，并提出一个置信度校准的方法以对其进行过滤和人工筛选。他们发现，即使进行了置信度校准，也会出现在测试数据集外的误差和挑战。因此，他们利用多篇文章进行预测，从而提高了预测的精度和可靠性。这项研究探讨了深度学习方法在文本挖掘中的应用和局限性，同时强调了在人工筛选过程中置信度校准的重要性。