MaskMark is a novel framework for image watermarking that offers two variants: MaskMark-D for global and local watermark extraction, and MaskMark-ED for enhanced robustness in localized areas. It employs a masking mechanism during the decoding and encoding stages to improve accuracy and adaptability while maintaining high visual quality. Experimental results demonstrate its superior performance over existing models, requiring significantly less computational cost.

Noisy labels can hinder the training of deep neural networks, leading to inaccuracies. The proposed $\epsilon$-softmax method modifies the softmax layer's outputs to approximate one-hot vectors with a controllable error, enhancing noise tolerance while maintaining a balance between robustness and effective learning through a combination with symmetric loss functions. Extensive experiments indicate its effectiveness in addressing both synthetic and real-world label noise.