Bac Nguyen
Profile
Bac is a research scientist at Sony AI who focuses his research on representation learning, vision-language models, and generative modeling. His work spans various application domains, such as text-to-speech and voice conversion, and he has made numerous important contributions to the field. Bac graduated summa cum laude with his Master of Science in Computer Science from Universidad Central de Las Villas in 2015, followed by a Ph.D. from Ghent University in 2019.
Publications
SONA: Learning Conditional, Unconditional, and Mismatching-Aware Discriminator
ICLR, 2026 | Yuhta Takida, Satoshi Hayakawa*, Takashi Shibuya, Masaaki Imaizumi*, Naoki Murata, Bac Nguyen, Toshimitsu Uesaka, Chieh-Hsin Lai, Yuki Mitsufuji
Deep generative models have made significant advances in generating complex content, yet conditional generation remains a fundamental challenge. Existing conditional generative adversarial networks often struggle to balance the dual objectives of assessing authenticity and c...
Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
ICLR, 2026 | Bac Nguyen, Yuhta Takida, Naoki Murata, Chieh-Hsin Lai, Toshimitsu Uesaka, Stefano Ermon*, Yuki Mitsufuji
Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between object slots and image content. We propose Contrastive Object-centric Diffusion Alignment (CODA), ...
G2D2: Gradient-Guided Discrete Diffusion for Image Inverse Problem Solving
TMLR, 2025 | Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Bac Nguyen, Stefano Ermon*, Yuki Mitsufuji
Recent literature has effectively leveraged diffusion models trained on continuous variables as priors for solving inverse problems. Notably, discrete diffusion models with discrete latent codes have shown strong performance, particularly in modalities suited for discrete co...
Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion
IJCNN, 2025 | Bac Nguyen, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji
By embedding discrete representations into a continuous latent space, we can leverage continuous-space latent diffusion models to handle generative modeling of discrete data. However, despite their initial success, most latent diffusion methods rely on fixed pretrained embed...
Sparo: Selective Attention for Robust and Compositional Transformer Encodings for Vision
ECCV, 2024 | Ankit Vani*, Bac Nguyen, Samuel Lavoie*, Ranjay Krishna*, Aaron Courville*
Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception allows us to robustly generalize under distractions and to new compositions of perceivable concepts. Transformers employ a similar notion...
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning
ECCV, 2024 | Bac Nguyen, Stefan Uhlich*, Fabien Cardinaux*, Lukas Mauch*, Marzieh Edraki*, Aaron Courville*
Handling distribution shifts from training data, known as out-of-distribution (OOD) generalization, poses a significant challenge in the field of machine learning. While a pre-trained vision-language model like CLIP has demonstrated remarkable zero-shot performance, further ...