Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge
Authors: Fawaz Sammani, Nikos Deligiannis
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We answer this question via an approach of textual concept-based explanations, showing their effectiveness, and perform an analysis encompassing a pool of 13 CLIP models varying in architecture, size and pretraining datasets. We explore those different aspects in relation to mutual knowledge, and analyze zero-shot predictions. Our approach demonstrates an effective and human-friendly way of understanding zero-shot classification decisions with CLIP. |
| Researcher Affiliation | Collaboration | Fawaz Sammani, Nikos Deligiannis ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium imec, Kapeldreef 75, B-3001 Leuven, Belgium EMAIL, EMAIL |
| Pseudocode | No | The paper describes the method step-by-step in narrative form, but does not include a formally labeled 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | Yes | 1https://github.com/fawazsammani/clip-interpret-mutual-knowledge |
| Open Datasets | Yes | We train these baselines on the full Image Net training set, and report the Top-1 and Top-5 accuracy results on the Image Net validation set in Table 5. |
| Dataset Splits | Yes | Models and Datasets: Our MI analysis considers a wide range of CLIP models varying in architecture, size and pretraining datasets, evaluated on the full Image Net validation split [30]. |
| Hardware Specification | Yes | All experiments are ran on a single NVIDIA RTX3090 GPU. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer [25]' and 'cosine schedule [34]' but does not provide specific version numbers for any libraries or frameworks used (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | The baselines are trained using the Adam optimizer [25] with a batch size of 64 and a learning rate of 1e-4 decayed using a cosine schedule [34] to 1e-5. We set q = 512. In section 3.1, we set k = 500 and τ = 1. For analyzing the mutual information and its dynamics in Section 4.1 in the main paper, we set the number of concepts L = 5 and consider the top 3 textual concepts for each visual concept. |