Towards unbiased, accurate and robust fine-tuning of zero-shot vision models
A foundational objective of machine learning is to create models that are (1) unbiased, ensuring fair predictions across different classes; (2) accurate, ex- celling in in-distribution (target) environments; and (3) robust, achieving high performance even under distribution shifts. Recently, vision...
Saved in:
Main Author: | Zhu Beier |
---|---|
Other Authors: | Hanwang Zhang |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/181746 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Zero-shot object detection and referring expression comprehension using vision-language models
by: A Manicka, Praveen
Published: (2024) -
Zero-shot text classification via self-supervised tuning
by: Liu, Chaoqun, et al.
Published: (2023) -
Generalized logit adjustment: Calibrating fine-tuned models by removing label bias in foundation models
by: ZHU, Beier, et al.
Published: (2023) -
Mitigating fine-grained hallucination by fine-tuning large vision-language models with caption rewrites
by: WANG, Lei, et al.
Published: (2024) -
Towards trustworthy recommenders: building explainable and unbiased recommendation systems
by: Hu, Yidan
Published: (2024)