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Found 7 papers, 2 papers with code
Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions
But different from CVAE, we present a method for disentangling the latent space into the label relevant and irrelevant dimensions, $\bm{\mathrm{z}}_s$ and $\bm{\mathrm{z}}_u$, for a single input.
Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation
The two types of synthesis, either label- or reference-based, have substantial differences.
Disentangling the Spatial Structure and Style in Conditional VAE
Depending on whether the label is related with the spatial structure, the output $z_s$ from the condition mapping network is used either as a style code or a spatial structure code.
Learning Posterior and Prior for Uncertainty Modeling in Person Re-Identification
This paper proposes to learn the sample posterior and the class prior distribution in the latent space, so that not only representative features but also the uncertainty can be built by the model.
Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays
Exploiting available medical records to train high performance computer-aided diagnosis (CAD) models via the semi-supervised learning (SSL) setting is emerging to tackle the prohibitively high labor costs involved in large-scale medical image annotations.
A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging
Lymph node (LN) metastasis status is one of the most critical prognostic and cancer staging factors for patients with resectable pancreatic ductal adenocarcinoma (PDAC), or in general, for any types of solid malignant tumors.
