Peihao Xiang

Submitted: 27 February 2025 You are already at the latest version This paper investigates the impact of randomly initialized unique encoding of classification label position on the visual masked self-supervised pre-trained model when fine-tuning downstream classification tasks. Our findings indicate that different random initializations lead to significant variations in fine-tuned results, even when using the same allocation strategy for classification datasets.