---
license: "apache-2.0"
pretty_name: Encoding Mismatch Analysis Data
tags:
- computer-vision
- vision-transformer
- knowledge-distillation
- representation-analysis
- encoding-mismatch
- spectral-energy-pattern
- pca
- svd
- icml-2026
- arxiv:2511.15572
configs:
- config_name: npz_array_catalog
  data_files:
  - split: train
    path: data/npz_array_catalog.csv
  default: true
- config_name: manifest
  data_files:
  - split: train
    path: data/manifest.csv
- config_name: cait_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/cait/sep/sep_thresholds.csv"
- config_name: comparison_sep_sep_comparison_table
  data_files:
  - split: train
    path: "raw/comparison/sep/sep_comparison_table.csv"
- config_name: deit_small_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/deit_small/sep/sep_thresholds.csv"
- config_name: swin_small_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/swin_small/sep/sep_thresholds.csv"
- config_name: vit_base_patch14_dinov2_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_base_patch14_dinov2/sep/sep_thresholds.csv"
- config_name: vit_base_patch16_224_dino_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_base_patch16_224_dino/sep/sep_thresholds.csv"
- config_name: vit_base_patch16_224_mae_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_base_patch16_224_mae/sep/sep_thresholds.csv"
- config_name: vit_base_patch16_clip_openai_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_base_patch16_clip_openai/sep/sep_thresholds.csv"
- config_name: vit_huge_patch14_224_mae_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_huge_patch14_224_mae/sep/sep_thresholds.csv"
- config_name: vit_large_21k_in1k_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_large_21k_in1k/sep/sep_thresholds.csv"
- config_name: vit_large_patch14_clip_openai_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_large_patch14_clip_openai/sep/sep_thresholds.csv"
- config_name: vit_large_patch14_dinov2_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_large_patch14_dinov2/sep/sep_thresholds.csv"
- config_name: vit_large_patch16_224_mae_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_large_patch16_224_mae/sep/sep_thresholds.csv"
- config_name: vit_small_patch16_224_dino_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_small_patch16_224_dino/sep/sep_thresholds.csv"
- config_name: vit_tiny_patch16_224_21k_sep_sep_thresholds
  data_files:
  - split: train
    path: "raw/vit_tiny_patch16_224_21k/sep/sep_thresholds.csv"
---

# Encoding Mismatch Analysis Data

This repository publishes the prepared numerical analysis artifacts associated
with **From Per-Image Low-Rank to Encoding Mismatch: Rethinking Feature
Distillation in Vision Transformers**. It is analysis data, not an image or
model-training dataset, and it does not redistribute ImageNet.

## Links

- Paper: https://arxiv.org/abs/2511.15572
- Hugging Face paper page: https://huggingface.co/papers/2511.15572
- Code and analysis scripts: https://github.com/thy960112/From-Per-Image-Low-Rank-to-Encoding-Mismatch
- Lift and WideLast checkpoints: https://huggingface.co/Huiyuancs/Encoding_Mismatch

## Load the default configuration

The default `npz_array_catalog` configuration has one row per safely inspected
array inside the original NPZ files. It records the source file, array key,
dtype, JSON-encoded shape, dimensionality, element count, finite numeric
summary statistics where applicable, a small JSON preview, and any safe
inspection error.

```python
from datasets import load_dataset

catalog = load_dataset(
    "Huiyuancs/Encoding_Mismatch_Analysis_Data",
    split="train",
)
```

## Load the manifest

The manifest records the repository-relative path, file type, byte size,
SHA-256 digest, and recommended loader for every artifact copied from the
GitHub repository's `Raw data/` directory.

```python
from datasets import load_dataset

manifest = load_dataset(
    "Huiyuancs/Encoding_Mismatch_Analysis_Data",
    "manifest",
    split="train",
)
```

## Load an original CSV table

Each original CSV has a separate configuration. For example:

```python
from datasets import load_dataset

table = load_dataset(
    "Huiyuancs/Encoding_Mismatch_Analysis_Data",
    "cait_sep_sep_thresholds",
    split="train",
)
```

## Download and read an original NPZ file

Use `hf_hub_download` for the original binary artifacts and keep NumPy's
pickle loading disabled:

```python
from huggingface_hub import hf_hub_download
import numpy as np

path = hf_hub_download(
    repo_id="Huiyuancs/Encoding_Mismatch_Analysis_Data",
    repo_type="dataset",
    filename="raw/cait/dataset_pca/dataset_pca_results.npz",
)

with np.load(path, allow_pickle=False) as archive:
    print(archive.files)
```

The same download method can be used with any `relative_path` from the
`manifest` configuration.

## Repository structure

```text
README.md
data/
├── manifest.csv
├── npz_array_catalog.csv
└── viewer_csv/              # only created when a source CSV needs it
raw/                         # byte-identical copy of Raw data/
```

`data/npz_array_catalog.csv` is a compact inspection index, not a replacement
for the original arrays. `data/manifest.csv` supplies checksums for verifying
the originals. All released CSV files load directly with Hugging Face Datasets, so their configurations point to the byte-identical files under `raw/`; no viewer-normalized copies were needed.

## CSV configurations

| Configuration | Original file | Config data file | Rows | Representation |
|---|---|---|---:|---|
| `cait_sep_sep_thresholds` | `raw/cait/sep/sep_thresholds.csv` | `raw/cait/sep/sep_thresholds.csv` | 5 | original |
| `comparison_sep_sep_comparison_table` | `raw/comparison/sep/sep_comparison_table.csv` | `raw/comparison/sep/sep_comparison_table.csv` | 14 | original |
| `deit_small_sep_sep_thresholds` | `raw/deit_small/sep/sep_thresholds.csv` | `raw/deit_small/sep/sep_thresholds.csv` | 5 | original |
| `swin_small_sep_sep_thresholds` | `raw/swin_small/sep/sep_thresholds.csv` | `raw/swin_small/sep/sep_thresholds.csv` | 5 | original |
| `vit_base_patch14_dinov2_sep_sep_thresholds` | `raw/vit_base_patch14_dinov2/sep/sep_thresholds.csv` | `raw/vit_base_patch14_dinov2/sep/sep_thresholds.csv` | 5 | original |
| `vit_base_patch16_224_dino_sep_sep_thresholds` | `raw/vit_base_patch16_224_dino/sep/sep_thresholds.csv` | `raw/vit_base_patch16_224_dino/sep/sep_thresholds.csv` | 5 | original |
| `vit_base_patch16_224_mae_sep_sep_thresholds` | `raw/vit_base_patch16_224_mae/sep/sep_thresholds.csv` | `raw/vit_base_patch16_224_mae/sep/sep_thresholds.csv` | 5 | original |
| `vit_base_patch16_clip_openai_sep_sep_thresholds` | `raw/vit_base_patch16_clip_openai/sep/sep_thresholds.csv` | `raw/vit_base_patch16_clip_openai/sep/sep_thresholds.csv` | 5 | original |
| `vit_huge_patch14_224_mae_sep_sep_thresholds` | `raw/vit_huge_patch14_224_mae/sep/sep_thresholds.csv` | `raw/vit_huge_patch14_224_mae/sep/sep_thresholds.csv` | 5 | original |
| `vit_large_21k_in1k_sep_sep_thresholds` | `raw/vit_large_21k_in1k/sep/sep_thresholds.csv` | `raw/vit_large_21k_in1k/sep/sep_thresholds.csv` | 5 | original |
| `vit_large_patch14_clip_openai_sep_sep_thresholds` | `raw/vit_large_patch14_clip_openai/sep/sep_thresholds.csv` | `raw/vit_large_patch14_clip_openai/sep/sep_thresholds.csv` | 5 | original |
| `vit_large_patch14_dinov2_sep_sep_thresholds` | `raw/vit_large_patch14_dinov2/sep/sep_thresholds.csv` | `raw/vit_large_patch14_dinov2/sep/sep_thresholds.csv` | 5 | original |
| `vit_large_patch16_224_mae_sep_sep_thresholds` | `raw/vit_large_patch16_224_mae/sep/sep_thresholds.csv` | `raw/vit_large_patch16_224_mae/sep/sep_thresholds.csv` | 5 | original |
| `vit_small_patch16_224_dino_sep_sep_thresholds` | `raw/vit_small_patch16_224_dino/sep/sep_thresholds.csv` | `raw/vit_small_patch16_224_dino/sep/sep_thresholds.csv` | 5 | original |
| `vit_tiny_patch16_224_21k_sep_sep_thresholds` | `raw/vit_tiny_patch16_224_21k/sep/sep_thresholds.csv` | `raw/vit_tiny_patch16_224_21k/sep/sep_thresholds.csv` | 5 | original |

## Source and intended use

The files are derived from the paper's representation-analysis workflow,
including per-image SVD, dataset-level PCA, and Spectral Energy Pattern
summaries. They are provided for inspecting the reported analyses and for
regenerating tables or figures with the corresponding GitHub scripts. The
artifacts are not a substitute for ImageNet-1K or for rerunning feature
extraction.

## License and third-party data

The repository content is released under `apache-2.0`. ImageNet images are not included; users remain responsible for the terms of ImageNet and all upstream software or model assets.

## Citation

```bibtex
@inproceedings{tian2026encodingmismatch,
  title     = {From Per-Image Low-Rank to Encoding Mismatch:
               Rethinking Feature Distillation in Vision Transformers},
  author    = {Tian, Huiyuan and Xu, Bonan and Li, Shijian},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  year      = {2026}
}
```