  This report is a reproducibility study of the paper "CDUL: CLIP-Driven
Unsupervised Learning for Multi-Label Image Classification" (Abdelfattah et al,
ICCV 2023). Our report makes the following contributions: (1) We provide a
reproducible, well commented and open-sourced code implementation for the
entire method specified in the original paper. (2) We try to verify the
effectiveness of the novel aggregation strategy which uses the CLIP model to
initialize the pseudo labels for the subsequent unsupervised multi-label image
classification task. (3) We try to verify the effectiveness of the
gradient-alignment training method specified in the original paper, which is
used to update the network parameters and pseudo labels. The code can be found
at https://github.com/cs-mshah/CDUL
