Recent advancements in Deep Neural Network (DNN) models have significantly
improved performance across computer vision tasks. However, achieving highly
generalizable and high-performing vision models requires extensive datasets,
leading to large storage requirements. This storage challenge poses a critical
bottleneck for scaling up vision models. Motivated by the success of discrete
representations, SeiT proposes to use Vector-Quantized (VQ) feature vectors
(i.e., tokens) as network inputs for vision classification. However, applying
traditional data augmentations to tokens faces challenges due to input domain
shift. To address this issue, we introduce TokenAdapt and ColorAdapt, simple
yet effective token-based augmentation strategies. TokenAdapt realigns token
embedding space for compatibility with spatial augmentations, preserving the
model's efficiency without requiring fine-tuning. Additionally, ColorAdapt
addresses color-based augmentations for tokens inspired by Adaptive Instance
Normalization (AdaIN). We evaluate our approach across various scenarios,
including storage-efficient ImageNet-1k classification, fine-grained
classification, robustness benchmarks, and ADE-20k semantic segmentation.
Experimental results demonstrate consistent performance improvement in diverse
experiments. Code is available at this https URL

深度神经网络在计算机视觉任务中取得了显著的性能提升，但实现高度可泛化和高性能的视觉模型需要大量的数据集，这对于扩展视觉模型来说是一个关键瓶颈。本文提出了一种基于向量量化的特征向量（即标记）作为视觉分类网络输入，以解决存储挑战，并通过引入 TokenAdapt 和 ColorAdapt 等简单而有效的基于标记的数据增强策略来解决输入域转移问题。实验证明了在各种场景下我们方法的一致性性能提升。

提升存储效率训练的伪造令牌

Forging Tokens for Improved Storage-efficient Training

Paraphrasing exemplifies the ability to abstract semantic content from
surface forms. Recent work on automatic paraphrasing is dominated by methods
leveraging Machine Translation (MT) as an intermediate step. This contrasts
with humans, who can paraphrase without being bilingual. This work proposes to
learn paraphrasing models from an unlabeled monolingual corpus only. To that
end, we propose a residual variant of vector-quantized variational
auto-encoder.
We compare with MT-based approaches on paraphrase identification, generation,
and training augmentation. Monolingual paraphrasing outperforms unsupervised
translation in all settings. Comparisons with supervised translation are more
mixed: monolingual paraphrasing is interesting for identification and
augmentation; supervised translation is superior for generation.

该论文提出了一种从非标记的单语语料库中学习的重述模型，通过在重述识别，生成和训练增强方面与基于机器翻译的方法进行比较，发现单语重述在所有场景中均优于无监督翻译。