Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?

This repository provides the codes for all experiments shown in the paper Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?.

Setup

• use Python 3.9
• run pip install -r requirements.txt

Compute embeddings

Use code from this folder to compute ImageNet and LAION CLIP embeddings.

Compute Imagenet Embedding

Run src/embeddings/imagenet/main.py to computes image embeddings, and store labels. No need to compute text embeddings because it's simply just a 1000 x n class matrix.

Compute LAION Embeddings

We use https://github.com/rom1504/clip-retrieval for calculating LAION embeddings.

DeDuplication of LAION400M

Run src/deduplication scripts to de-duplicate LAION400M and getting it to 200M datapoints. The main scripts are as follows: - assign_clusters.py: does k means and assigns clusters to CLIP embeddings. - save_cluster_embeddings_and_similarities.py: saves embeddings of the same cluster to a single file. - deduplicate.py: deduplicates and then gives out the deduplicated paths for each cluster, which can be combined for sampling.

Compute similarities between datasets and get paths for pruned datasets

Run src/sims_and_paths scripts to compute similarities of eval datasets to LAION in the CLIP embedding space and get paths. - compute_sims.py : compute similarities for one small laion or imagenet-train embedding chunk to a given dataset and get top k candidates per eval datapoint. - combine_sims.py : combine all the top k similarities of the chunks to get top k overall candidates in LAION. - compute_max_sims.py : compute the max similarity for each datapoint one small laion or imagenet-train embedding chunk to a given dataset. - combine_max_sims.py : simple concatenating of max sims. - get_paths_chunk.py: using the above candidates that pass the imagenet-train to imagenet-x threshold, we obtain path chunks of sub-sampled dataset. - get_paths.ipynb : combine the path chunks to get paths for the pruned datasets.

Sampling

Run src/sampling/subsample_dataset.py with a given paths set (LAION paths in .npy format) to get the final subsampled LAION dataset.

Training

For training on all subsampled datasets we use: https://github.com/mlfoundations/open_clip. We change total batchsize to 33,600.

Eval

Run src/eval scripts to evaluate model on several eval datasets like ImageNet-Sketch/Val/R/V2/A and ObjectNet.

Citation

If you find the insights from the paper or our code base useful, please cite: @inproceedings{mayilvahanan2024does, title={Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?}, author={Prasanna Mayilvahanan and Thadd{\"a}us Wiedemer and Evgenia Rusak and Matthias Bethge and Wieland Brendel}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=tnBaiidobu} }