A progressive multi-scale convolutional block with growing dilation rates, placed in an encoder-decoder-critic steganography network, is claimed to improve image quality metrics; no steganalysis scores are reported.
Improved Training with Curriculum GANs
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abstract
In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation. We also show evidence that this strategy may be broadly applicable to improving GAN training in other data modalities.
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cs.CV 1years
2025 1verdicts
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CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learning
A progressive multi-scale convolutional block with growing dilation rates, placed in an encoder-decoder-critic steganography network, is claimed to improve image quality metrics; no steganalysis scores are reported.