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Found 4 papers, 2 papers with code
Fairness by Learning Orthogonal Disentangled Representations
We explicitly enforce the meaningful representation to be agnostic to sensitive information by entropy maximization.
Deep Attention Based Semi-Supervised 2D-Pose Estimation for Surgical Instruments
To this end, a lightweight network architecture is introduced and mean teacher, virtual adversarial training and pseudo-labeling algorithms are evaluated on 2D-pose estimation for surgical instruments.
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine.
Learning Interpretable Disentangled Representations using Adversarial VAEs
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice.
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