18 citations · 32 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Textile Pattern Generation Using Diffusion Models
Halil Faruk Karagoz, Gulcin Baykal, Irem Arikan Eksi +1
The problem of text-guided image generation is a complex task in Computer Vision, with various applications, including creating visually appealing artwork and realistic product ima…
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks
Gulcin Baykal, Furkan Ozcelik, Gozde Unal
Generative Adversarial Networks (GANs) have become the most used networks towards solving the problem of image generation. Self-supervised GANs are later proposed to avoid the cata…
DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning
Gulcin Baykal, Gozde Unal
Generative Adversarial Networks (GANs) triggered an increased interest in problem of image generation due to their improved output image quality and versatility for expansion towar…