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cs.CV2024
Conditioning GAN Without Training Dataset
Kidist Amde Mekonnen
Deep learning algorithms have a large number of trainable parameters often with sizes of hundreds of thousands or more. Training this algorithm requires a large amount of training…
cs.CV2024★ 1 cited
Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Sampling
Kidist Amde Mekonnen, Nicola Dall'Asen, Paolo Rota
Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models, achieving remarkable performance in image synthesis tasks. However, these models f…
cs.CV2023★ 1 cited
Balanced Face Dataset: Guiding StyleGAN to Generate Labeled Synthetic Face Image Dataset for Underrepresented Group
Kidist Amde Mekonnen
For a machine learning model to generalize effectively to unseen data within a particular problem domain, it is well-understood that the data needs to be of sufficient size and rep…