In weakly-supervised text classification, only label names act as sources of
supervision. Predominant approaches to weakly-supervised text classification
utilize a two-phase framework, where test samples are first assigned
pseudo-labels and are then used to train a neural text classifier. In most
previous work, the pseudo-labeling step is dependent on obtaining seed words
that best capture the relevance of each class label. We present LIME, a
framework for weakly-supervised text classification that entirely replaces the
brittle seed-word generation process with entailment-based
pseudo-classification. We find that combining weakly-supervised classification
and textual entailment mitigates shortcomings of both, resulting in a more
streamlined and effective classification pipeline. With just an off-the-shelf
textual entailment model, LIME outperforms recent baselines in
weakly-supervised text classification and achieves state-of-the-art in 4
benchmarks. We open source our code at this https URL.

LIME 是一种基于蕴涵关系的伪分类方式，可取代传统脆弱的种子词生成过程，结合弱监督文本分类和文本蕴涵建模来优化分类流程，通过实验取得了史上最优结果。

LIME: 无种子弱监督文本分类

LIME: Weakly-Supervised Text Classification Without Seeds

We present a simple and effective way to estimate the batch-norm statistics
during test time, to fast adapt a source model to target test samples. Known as
Test-Time Adaptation, most prior works studying this task follow two
assumptions in their evaluation where (1) test samples come together as a large
batch, and (2) all from a single test distribution. However, in practice, these
two assumptions may not stand, the reasons for which we propose two new
evaluation settings where batch sizes are arbitrary and multiple distributions
are considered. Unlike the previous methods that require a large batch of
single distribution during test time to calculate stable batch-norm statistics,
our method avoid any dependency on large online batches and is able to estimate
accurate batch-norm statistics with a single sample. The proposed method
significantly outperforms the State-Of-The-Art in the newly proposed settings
in Test-Time Adaptation Task, and also demonstrates improvements in various
other settings such as Source-Free Unsupervised Domain Adaptation and Zero-Shot
Classification.

本文提出了一种简单而有效的方法，可以在测试期间估算批量标准化统计量以快速适应目标测试样本，并提出两种新的评估设置，避免对大量在线批次的依赖并能够仅通过单个样本估算准确的批量标准化统计量。该方法在测试时间自适应任务的新提出的设置中明显优于现有方法，并且在其他设置中也有所改进。

MixNorm: 通过在线归一化估计进行测试时适应

MixNorm: Test-Time Adaptation Through Online Normalization Estimation

Intuitively, one would expect accuracy of a trained neural network's
prediction on test samples to correlate with how densely the samples are
surrounded by seen training samples in representation space. We find that a
bound on empirical training error smoothed across linear activation regions
scales inversely with training sample density in representation space.
Empirically, we verify this bound is a strong predictor of the inaccuracy of
the network's prediction on test samples. For unseen test sets, including those
with out-of-distribution samples, ranking test samples by their local region's
error bound and discarding samples with the highest bounds raises prediction
accuracy by up to 20% in absolute terms for image classification datasets, on
average over thresholds.

通过研究表明，在表示空间中，训练样本密度的倒数平滑处理后的经验训练误差边界与测试样本的预测不准确性密切相关，通过在测试集中排列样本边界值并放弃最高边界值的样本，平均阈值下对图像分类数据集的预测准确性提高了 20％。