In recent years, image style transfer has matured significantly in the field of computer vision. However, current methods for image style transfer still face the challenge of balancing between content edge contours and style texture strokes, as it is difficult to control the degree of stylization. To address this issue, we propose a UNet based on Transformer feature fusion, named Trans-DCUNet. The network adaptively integrates content and style features by taking advantage of the self-learning characteristics of the cross-attention mechanism in the Transformer. The network combines Transformer and CNN, which takes advantage of the global context capture ability of Transformers and the local modeling ability of convolutional neural networks to fully learn image features at multiple levels, thereby generating stylized images. To enhance texture generation, we introduce a deformable convolutional residual module, which allows the convolution kernel to adapt to varying image features, capturing fine texture details more effectively. Additionally, we augment the traditional perception loss with edge detection loss and frequency perception loss, aiming to better preserve the edge contours of the content image and learn the texture strokes of the style image. Our experiments were conducted on the Microsoft COCO and WikiArt datasets. Experimental results show that our method achieves a content retention SSIM of up to 0.8655 and a style similarity LPIPS of 0.5655, outperforming most competing methods, while generating more artistic stylized images with significantly improved visual effects.
Yingjie Zhao, Libo Xu, Chaoyi Pang et al.· Scientific Reports· 0 citations
: High-stakes clinical decision support (CDS) demands a property that aggregate accuracy cannot capture: a trace that a clinician who was not in the room can inspect layer by layer when the system is wrong. We argue that the way to obtain this property is to refuse to entangle the large language model (LLM) with the rest of the pipeline. We propose KRD (Knowledge–Rule–Decision) , a four-component architecture that separates fact extraction, a compile-time clinical knowledge layer in the spirit of the LLM Wiki pattern of Karpathy, a rule layer of hand-written contraindications and heuristics, and a decision interface whose compose method short-circuits to a rule-cited blocking response whenever any hard violation fires. We evaluate KRD against a pure language model, a retrieval-augmented language model, a rule-only system, and a light hybrid on a benchmark of 32 type-1 diabetes scenarios. A strict version of the unsafe-suggestion rate stratifies the five systems monotonically into four distinct tiers from 0.867 down to zero, with S4 and S5 tied at the floor; the full KRD stack and the light hybrid reach the hard-safety ceiling together; KRD leads the light hybrid on evidence trace completeness by 25% relative and on reviewer correction burden by 12% relative, both directionally clear and borderline significant under bootstrap intervals; and KRD issues 17 language model calls per benchmark pass against the light hybrid’s 32, a 47% reduction that is a direct consequence of the architectural choice to evaluate the rule layer before invoking the model. We also report honestly that the evidence gate is inert on this benchmark because every compiled concept is graded A or B, and we trace five fact-extraction failures to a single field and a single linguistic pattern. The contribution is not that KRD is universally optimal but that layer-wise auditability is a design discipline whose cost in this setting was lower than its critics would have predicted.
Bailing Zhang, Genlang Chen· Journal of Intelligent Medic...· 0 citations
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