2 citations · 3 across the 20 of their papers we have counts for
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Consistent Distribution Matching for Data-Free Diffusion Distillation
Yuxiang Fu, Qi Yan, Zike Wu +4
Flow and diffusion models suffer from slow inference due to computationally expensive numerical integration. Distillation provides a promising way for a student model to learn from…
ProtoTTA: Prototype-Guided Test-Time Adaptation
Mohammad Mahdi Abootorabi, Parvin Mousavi, Purang Abolmaesumi +1
Deep networks that rely on prototypes-interpretable representations that can be related to the model input-have gained significant attention for balancing high accuracy with inhere…
ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion
Nima Kondori, Hanwen Liang, Hooman Vaseli +5
Synthetic data generation represents a significant advancement in boosting the performance of machine learning (ML) models, particularly in fields where data acquisition is challen…
Federated Impression for Learning with Distributed Heterogeneous Data
Atrin Arya, Sana Ayromlou, Armin Saadat +2
Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Fed…
CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning
Sana Ayromlou, Teresa Tsang, Purang Abolmaesumi +1
In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from al…