Tzu-Heng (Brian) Huang
@zihengh1.bsky.social
CS Ph.D. Student @UWMadison. Research Intern @Apple AIML. Focusing on multimodal models, data curation, and data-centric AI.
zihengh1.github.io
zihengh1.github.io
Our results are exciting! 🎉
✔️ Pretrained model weights are reliable guides for data selection.
✔️ Grad-Mimic identifies noisy samples and estimates training dataset quality.
✔️ It even complements other filtering methods, boosting CLIP performance with less data!
✔️ Pretrained model weights are reliable guides for data selection.
✔️ Grad-Mimic identifies noisy samples and estimates training dataset quality.
✔️ It even complements other filtering methods, boosting CLIP performance with less data!
February 9, 2025 at 9:08 PM
Our results are exciting! 🎉
✔️ Pretrained model weights are reliable guides for data selection.
✔️ Grad-Mimic identifies noisy samples and estimates training dataset quality.
✔️ It even complements other filtering methods, boosting CLIP performance with less data!
✔️ Pretrained model weights are reliable guides for data selection.
✔️ Grad-Mimic identifies noisy samples and estimates training dataset quality.
✔️ It even complements other filtering methods, boosting CLIP performance with less data!
Mimic Score helps identify samples that can misguide weight updates. We can automatically filter these out, improving training. Here is an identified example using noisy web datasets!
February 9, 2025 at 9:08 PM
Mimic Score helps identify samples that can misguide weight updates. We can automatically filter these out, improving training. Here is an identified example using noisy web datasets!
🛠️ Using Mimic Score, we develop Grad-Mimic, a two-stage framework:
1️⃣ Training Phase: Prioritizes which samples to learn, boosting data efficiency.
2️⃣ Post-Training Phase: Evaluates sample utility across training steps, creating an ensemble filter using weak supervision.
1️⃣ Training Phase: Prioritizes which samples to learn, boosting data efficiency.
2️⃣ Post-Training Phase: Evaluates sample utility across training steps, creating an ensemble filter using weak supervision.
February 9, 2025 at 9:08 PM
🛠️ Using Mimic Score, we develop Grad-Mimic, a two-stage framework:
1️⃣ Training Phase: Prioritizes which samples to learn, boosting data efficiency.
2️⃣ Post-Training Phase: Evaluates sample utility across training steps, creating an ensemble filter using weak supervision.
1️⃣ Training Phase: Prioritizes which samples to learn, boosting data efficiency.
2️⃣ Post-Training Phase: Evaluates sample utility across training steps, creating an ensemble filter using weak supervision.
📣 We propose the Mimic Score: a new data quality metric. It leverages reference model weights to assess sample utility, relying on the alignment between gradients and a target direction induced by the reference model.
February 9, 2025 at 9:08 PM
📣 We propose the Mimic Score: a new data quality metric. It leverages reference model weights to assess sample utility, relying on the alignment between gradients and a target direction induced by the reference model.