| --- |
| license: cc-by-nc-4.0 |
| library_name: pytorch |
| tags: |
| - sparse-autoencoder |
| - concept-bottleneck |
| - interpretability |
| - mechanistic-interpretability |
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| |
| # SAE–CBM Unification — SAE and backbone checkpoints |
|
|
| Sparse autoencoders trained on frozen backbone features, plus the fine-tuned backbones |
| they were trained on, for the unification of sparse dictionaries and concept bottleneck |
| models. |
|
|
| ## Contenu |
|
|
| 231 fichiers `.pt`, 38.3 Go. |
|
|
| | Axe | Valeurs | |
| | --- | --- | |
| | Datasets | `cub`, `awa2`, `paco`, `skincon`, `imagenet` | |
| | Backbones | `resnet50` (layer4), `vit_b_16` (penultimate), `dinov2_vitb14_reg` (penultimate) | |
| | Seeds | `0`, `42`, `2026` | |
| | Dictionnaire | TopK, K ∈ {1536, 4096} | |
|
|
| Arborescence : |
|
|
| ``` |
| results/metrics_curve_cov2/<dataset>/seed_<s>/ |
| backbone_<dataset>_<backbone>.pt # backbone fine-tuné |
| metrics_curve_<DS>_<backbone>_s<s>_<ts>.csv # courbe de métriques |
| sae_artifacts_<DS>_topk_K<K>_<norm>_<layer>_<ts>/ |
| sae_<layer>_K<K>_topk_<norm>_<ts>_<lambda>.pt |
| results/metrics_curve_<dataset>/seed_<s>/ # série antérieure (cov1) |
| code/src/cbm_pipeline/ # code d'inférence |
| ``` |
|
|
| ## Format des checkpoints SAE |
|
|
| `torch.load(path, map_location="cpu")` renvoie un dict : |
|
|
| | Clé | Contenu | |
| | --- | --- | |
| | `sae_state_dict` | `dictionary._weights`, `dictionary.multiplier`, `encoder.final_block.0.{weight,bias}` | |
| | `feat_to_concept_state_dict` | tête features → concepts | |
| | `concept_clf_state_dict` | classifieur concept (`fc.weight`, `fc.bias`) | |
| | `W_dec`, `W_cbm`, `Z` | décodeur, matrice CBM, codes | |
| | `embeddings` | activations mises en cache : `H_log`, `H_prob`, splits `train`/`test` | |
| | métadonnées | `layer`, `method` (`topk`), `normal`, `lambda`, `split` | |
|
|
| > Les `.pt` embarquent des **activations en cache** (`embeddings`) en plus des poids — |
| > c'est ce qui explique leur taille. Un export poids-seuls serait nettement plus léger. |
|
|
| ## Chargement |
|
|
| ```python |
| import torch |
| ckpt = torch.load("results/.../sae_penultimate_K1536_topk_normal_<ts>_<lambda>.pt", map_location="cpu") |
| sae_weights = ckpt["sae_state_dict"] |
| W_dec = ckpt["W_dec"] |
| ``` |
|
|
| Le code d'inférence est sous `code/src/cbm_pipeline/`. |
|
|
| ## `cub/` — features CUB et reconstructions (self-contained) |
|
|
| De quoi vérifier les SAE CUB sans rien recalculer : les features d'entrée, la |
| reconstruction, et les codes. |
|
|
| ``` |
| cub/features/<backbone>/{train,val,test}.npy # [N, D] fp32, entrée du SAE |
| cub/features/<backbone>/{train,val,test}_labels.npy # [N] int64, classe |
| cub/features/<backbone>/meta.json |
| cub/reconstruction/<sae_tag>/{train,val,test}_xhat.npy # [N, D] fp32 |
| cub/reconstruction/<sae_tag>/{train,val,test}_l0.npy # [N] int32, L0 par échantillon |
| cub/reconstruction/<sae_tag>/metrics.json |
| cub/summary.json |
| ``` |
|
|
| Splits CUB-200-2011 : train 4796 / val 1198 / test 5794 (`val_ratio=0.2`, 200 classes). |
|
|
| ### Fidélité de reconstruction (FVU, seed 0) |
|
|
| | Backbone | Couche | D | K | top-k | train | val | test | |
| | --- | --- | --- | --- | --- | --- | --- | --- | |
| | `resnet50` | layer4 | 2048 | 4096 | 20 | 0.179 | 0.178 | 0.253 | |
| | `vit_b_16` | penultimate | 768 | 1536 | 8 | 0.267 | 0.271 | 0.371 | |
| | `dinov2_vitb14_reg` | penultimate | 768 | 1536 | 8 | 0.178 | 0.185 | 0.232 | |
|
|
| FVU = `||x - x̂||² / ||x - mean(x)||²` (0 = parfait, 1 = prédicteur moyen). Le L0 |
| mesuré vaut exactement le `top_k` visé sur tous les échantillons. |
|
|
| > `vit_b_16` et `dinov2_vitb14_reg` produisent tous deux des dossiers |
| > `sae_artifacts_CUB_topk_K1536_normal_penultimate_<ts>` en dimension 768. Seul le |
| > timestamp les distingue, apparié via `metrics_curve_CUB_<backbone>_s0_<ts>.csv` : |
| > `124812` = ViT-B/16, `124813` = DINOv2. |
|
|
| Reproduction : `code/scripts/cub_cbm_features_recon.py` (+ `run_cub.slurm`). |
|
|
| ## Features d'entrée ImageNet |
|
|
| Les features backbone ImageNet fp32 sont publiées séparément — voir le repo |
| `imagenet-backbone-features-fp32` (même gating). |
|
|
| ## Citation |
|
|
| TODO — référence de l'article. |
|
|
