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Found 4 papers, 0 papers with code
ENTED: Enhanced Neural Texture Extraction and Distribution for Reference-based Blind Face Restoration
The latent code extracted from the degraded input image often contains corrupted features, making it difficult to align the semantic information from the input with the high-quality textures from the reference.
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models
Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules to inject the RAW information into the diffusion denoising process via modulating the intermediate features of UNet.
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification
This paper provides a new perspective to explain the success of knowledge distillation, i. e., quantifying knowledge points encoded in intermediate layers of a DNN for classification, based on the information theory.
Explaining Knowledge Distillation by Quantifying the Knowledge
Whereas, in the scenario of learning from raw data, the DNN learns visual concepts sequentially.
