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Found 4 papers, 2 papers with code
Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
To mitigate the impact of data heterogeneity on FL performance, we start with analyzing how FL training influence FL performance by decomposing the global loss into three terms: local loss, distribution shift loss and aggregation loss.
Fine-Grained Image Style Transfer with Visual Transformers
Such a design usually destroys the spatial information of the input images and fails to transfer fine-grained style patterns into style transfer results.
Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers
Given a sequence of style tokens, the TokenGAN is able to control the image synthesis by assigning the styles to the content tokens by attention mechanism with a Transformer.
Image Semantic Transformation: Faster, Lighter and Stronger
We propose Image-Semantic-Transformation-Reconstruction-Circle(ISTRC) model, a novel and powerful method using facenet's Euclidean latent space to understand the images.
