Zero-Shot Distillation for Image Encoders: How to Make Effective Use of
Synthetic Data

Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data

25 April 2024

Matthias Hein

Papers citing "Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data"

6 / 6 papers shown

Title
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training? Hasan Hammoud Hani Itani Fabio Pizzati Philip H. S. Torr Adel Bibi Bernard Ghanem CLIP VLM 112 35 0 02 Feb 2024
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images Zhuoran Yu Chenchen Zhu Sean Culatana Raghuraman Krishnamoorthi Fanyi Xiao Yong Jae Lee 109 13 0 04 Dec 2023
LCM-LoRA: A Universal Stable-Diffusion Acceleration Module Simian Luo Yiqin Tan Suraj Patil Daniel Gu Patrick von Platen Apolinário Passos Longbo Huang Jian Li Hang Zhao MoMe 108 144 0 09 Nov 2023
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models Andy Zhou Jindong Wang Yu-xiong Wang Haohan Wang VLM 36 6 0 02 Nov 2023
Identification of Systematic Errors of Image Classifiers on Rare Subgroups J. H. Metzen Robin Hutmacher N. G. Hua Valentyn Boreiko Dan Zhang AAML VLM 56 18 0 09 Mar 2023
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models Junnan Li Dongxu Li Silvio Savarese Steven C. H. Hoi VLM MLLM 259 4,223 0 30 Jan 2023