no code implementations • 21 Mar 2024 • Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark Hasegawa-Johnson, Yingzhen Li, Chang D. Yoo
Through a series of observations, we find that the prompt choice significantly affects the calibration in CLIP, where the prompts leading to higher text feature dispersion result in better-calibrated predictions.
no code implementations • 30 Nov 2023 • Axi Niu, Kang Zhang, Joshua Tian Jin Tee, Trung X. Pham, Jinqiu Sun, Chang D. Yoo, In So Kweon, Yanning Zhang
It is well known the adversarial optimization of GAN-based image super-resolution (SR) methods makes the preceding SR model generate unpleasant and undesirable artifacts, leading to large distortion.
1 code implementation • 4 Mar 2023 • Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon, Sunjae Yoon, Gwangsu Kim, Yingzhen Li, Chang D. Yoo
Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions.
no code implementations • 17 Nov 2022 • Trung X. Pham, Axi Niu, Zhang Kang, Sultan Rizky Madjid, Ji Woo Hong, Daehyeok Kim, Joshua Tian Jin Tee, Chang D. Yoo
To solve this problem, we propose "residual momentum" to directly reduce this gap to encourage the student to learn the representation as close to that of the teacher as possible, narrow the performance gap with the teacher, and significantly improve the existing SSL.