An important goal of self-supervised learning is to enable model pre-training to benefit from almost unlimited data. However, one method that has recently become popular, namely masked image modeling (MIM), is suspected to be unable to benefit from larger data. In this work, we break thi...
在ImageNet1k上换算就是200,400,800epochs。 2.2.Pretrained实验结论: 上图表示在不同训练时长下在ImageNet1k上的精度: 首先说下simmim预训练方法的一个性质:能够用较少的数据跟用大量数据的有监督学习的精度持平。 1)第二列:Swin_L比Swin_H精度高,因为后者在IN1k20%出现过拟合; 2)当IN1k...
合成数据的滥用可能会扩散错误信息 Misuse of synthetic data might proliferate misinformation 合成数据可能会导致人工智能目标的模糊性Synthetic data might cause ambiguity in AI alignment Training with synthetic data makes evaluation decontamination harder Directions for Future Work Synthetic data scaling Further imp...
Embodiments of the present disclosure relate to a sensor interface circuit that performs scaling of image data in a Bayer pattern without spreading defective pixels across multiple pixels. The sensor interface circuit may include a register circuit storing operating parameters of the sensor interface circ...
2024 arXiv Data Driven D3: Scaling Up Deepfake Detection by Learning from Discrepancy - 2024 arXiv Space Domain Band-Attention Modulated RetNet for Face Forgery Detection - 2024 arXiv Space Domain Diffusion Facial Forgery Detection - 2024 arXiv Space Domain Masked Conditional Diffusion Model for ...
2.5 Causality to Bring Insights to NLP Modeling (for Robustness, Domain Adaptation, etc)(2023 ICML) Towards Trustworthy Explanation: On Causal Rationalization. Wenbo Zhang, Tong Wu, Yunlong Wang, Yong Cai, Hengrui Cai.[pdf] (2023) Towards Trustworthy and Aligned Machine Learning: A Data-centric...
There are also methods based on image restoration, which train an encoder-decoder network to reconstruct pixel values of masked input data6. In general, when the amount of annotated data is small, a network initialized with pretrained weights can achieve better classification performance than a ...
RingMo: a remote sensing foundation model with masked image modeling IEEE Trans. Geosci. Remote Sens., 61 (2023), pp. 1-22, 10.1109/TGRS.2022.3194732 View in ScopusGoogle Scholar Tadono et al., 2016 T. Tadono, H. Nagai, H. Ishida, F. Oda, S. Naito, K. Minakawa, H. Iwamoto Ge...
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Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data, ESANN'20,Daniele Castellana, Davide Bacciu Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation, Frotiers in Robotics and AI,Valencia, Angel J., and Pierre Payeur ...