4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.CV2023
Mitigating stereotypical biases in text to image generative systems
Piero Esposito, Parmida Atighehchian, Anastasis Germanidis +1
State-of-the-art generative text-to-image models are known to exhibit social biases and over-represent certain groups like people of perceived lighter skin tones and men in their o…
cs.CL2023★ 4 cited
Cabrita: closing the gap for foreign languages
Celio Larcher, Marcos Piau, Paulo Finardi +3
The strategy of training the model from scratch in a specific language or domain serves two essential purposes: i) enhancing performance in the particular linguistic or domain cont…