SeiT++: Masked Token Modeling Improves Storage-efficient Training

• URL: http://arxiv.org/abs/2312.10105v5
• Date: Mon, 12 Aug 2024 08:41:10 GMT
• Title: SeiT++: Masked Token Modeling Improves Storage-efficient Training
• Authors: Minhyun Lee, Song Park, Byeongho Heo, Dongyoon Han, Hyunjung Shim,
• Abstract summary: Recent advancements in Deep Neural Network (DNN) models have significantly improved performance across computer vision tasks. achieving highly generalizable and high-performing vision models requires expansive datasets, resulting in significant storage requirements. Recent breakthrough by SeiT proposed the use of Vector-Quantized (VQ) feature vectors (i.e., tokens) as network inputs for vision classification. In this paper, we extend SeiT by integrating Masked Token Modeling (MTM) for self-supervised pre-training.
• Score: 36.95646819348317
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 expansive datasets, resulting in significant storage requirements. This storage challenge is a critical bottleneck for scaling up models. A recent breakthrough by SeiT proposed the use of Vector-Quantized (VQ) feature vectors (i.e., tokens) as network inputs for vision classification. This approach achieved 90% of the performance of a model trained on full-pixel images with only 1% of the storage. While SeiT needs labeled data, its potential in scenarios beyond fully supervised learning remains largely untapped. In this paper, we extend SeiT by integrating Masked Token Modeling (MTM) for self-supervised pre-training. Recognizing that self-supervised approaches often demand more data due to the lack of labels, we introduce TokenAdapt and ColorAdapt. These methods facilitate comprehensive token-friendly data augmentation, effectively addressing the increased data requirements of self-supervised learning. We evaluate our approach across various scenarios, including storage-efficient ImageNet-1k classification, fine-grained classification, ADE-20k semantic segmentation, and robustness benchmarks. Experimental results demonstrate consistent performance improvement in diverse experiments, validating the effectiveness of our method. Code is available at https://github.com/naver-ai/seit.

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