2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CV2024★ 1 cited
Disentanglement with Factor Quantized Variational Autoencoders
Gulcin Baykal, Melih Kandemir, Gozde Unal
Disentangled representation learning aims to represent the underlying generative factors of a dataset in a latent representation independently of one another. In our work, we propo…
cs.CV2023
EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders
Gulcin Baykal, Melih Kandemir, Gozde Unal
Codebook collapse is a common problem in training deep generative models with discrete representation spaces like Vector Quantized Variational Autoencoders (VQ-VAEs). We observe th…
cs.CV2023★ 2 cited
ProtoDiffusion: Classifier-Free Diffusion Guidance with Prototype Learning
Gulcin Baykal, Halil Faruk Karagoz, Taha Binhuraib +1
Diffusion models are generative models that have shown significant advantages compared to other generative models in terms of higher generation quality and more stable training. Ho…