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Found 5 papers, 2 papers with code
Towards Practical Non-Adversarial Distribution Alignment via Variational Bounds
Distribution alignment can be used to learn invariant representations with applications in fairness and robustness.
Assessment of Anterior Cruciate Ligament Injury Risk Based on Human Key Points Detection Algorithm
This paper aims to detect the potential injury risk of the anterior cruciate ligament (ACL) by proposing an ACL potential injury risk assessment algorithm based on key points of the human body detected using computer vision technology.
Cooperative Distribution Alignment via JSD Upper Bound
Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned distribution given only samples from each distribution.
Why be adversarial? Let's cooperate!: Cooperative Dataset Alignment via JSD Upper Bound
Unsupervised dataset alignment estimates a transformation that maps two or more source domains to a shared aligned domain given only the domain datasets.
Iterative Alignment Flows
Existing flow-based approaches estimate multiple flows independently, which is equivalent to learning multiple full generative models.
