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

arXiv:2312.10105v3 Announce Type: replace
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 …

arxiv cs.cv modeling storage token training type