diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4013985b0f4cb171bb9074283a918956a27c978d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,230 @@
+---
+license: "apache-2.0"
+pretty_name: Encoding Mismatch Analysis Data
+task_categories:
+- image-classification
+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}
+}
+```
diff --git a/data/manifest.csv b/data/manifest.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8cee505b1b5c299e7e5e00866f75f7a437105353
--- /dev/null
+++ b/data/manifest.csv
@@ -0,0 +1,60 @@
+relative_path,file_type,size_bytes,sha256,recommended_loader
+raw/cait/dataset_pca/dataset_pca_results.npz,npz,1288003,a39d65a703d9e3500fc20b778577f578e57fa3940b8f399afb8ce4c0a25442d2,hf_hub_download + numpy.load(allow_pickle=False)
+raw/cait/sep/sep_results.npz,npz,5457,83993dbd86038994ed481bbcefc46010364671a14debddc0b7cfa02a8a02007f,hf_hub_download + numpy.load(allow_pickle=False)
+raw/cait/sep/sep_thresholds.csv,csv,218,f06309001009bf39fd565e64350d0578a291ee732c6daa6d45d294708c7ef69a,datasets.load_dataset or pandas.read_csv
+raw/cait/svd/svd_results.npz,npz,42654,e42f86cbcc728100280cde6b4aaf66dd15e3ce32ed7c37b6a722a7a4b005a584,hf_hub_download + numpy.load(allow_pickle=False)
+raw/comparison/sep/sep_comparison_table.csv,csv,1415,2e3dedcc9fab824b7afd715c6f7013d84ddfb5cefa928e09ab6a972843a1b5c2,datasets.load_dataset or pandas.read_csv
+raw/comparison/svd_pca/svd_rank_table.md,md,1215,2cb6bdee3329cd996c8aa0b567b48eb24bb9006e5abca861c13e93f53f961b59,hf_hub_download
+raw/comparison/svd_pca/svd_rank_table.tex,tex,1361,4c329b24e61fe60578e4d8b00cd48c378b400650c7c3ed9f972ff3594ad9cf34,hf_hub_download
+raw/deit_small/dataset_pca/dataset_pca_results.npz,npz,1309567,66b1c629f4212247c5b48cd36553911111938e525574997d05f8ce568c0969be,hf_hub_download + numpy.load(allow_pickle=False)
+raw/deit_small/sep/sep_results.npz,npz,5465,c715edce4500aad0d27acafb2dda9a8ed4ab4f15ae067d178170084da783c7a4,hf_hub_download + numpy.load(allow_pickle=False)
+raw/deit_small/sep/sep_thresholds.csv,csv,228,58ec0dba4fb589619c4fdf134e77070072a047f00d8674fc2874eb26e0c18503,datasets.load_dataset or pandas.read_csv
+raw/deit_small/svd/svd_results.npz,npz,42654,15ee63b1f81c636917046ae3fe36858cd3952f9b1e6f495900133447dd4167e2,hf_hub_download + numpy.load(allow_pickle=False)
+raw/swin_small/dataset_pca/dataset_pca_results.npz,npz,2392755,09a9499ed8aca772c4b6a8edf800f78273ccbf4d17cf80fa03fbabde4ef60a86,hf_hub_download + numpy.load(allow_pickle=False)
+raw/swin_small/sep/sep_results.npz,npz,8537,b04e791c227b7734054710608a1833f7e23c98c56e6b3e500cb00e4b2289b6e8,hf_hub_download + numpy.load(allow_pickle=False)
+raw/swin_small/sep/sep_thresholds.csv,csv,228,70f49d860aacddc7bfccae07259d85590d87f5aa0a853dcc69a7d9ae92cbc3a5,datasets.load_dataset or pandas.read_csv
+raw/swin_small/svd/svd_results.npz,npz,42654,599e9ffc099e35fc97be45789bb7015e5dc6619402a412a9026001be38490bfd,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch14_dinov2/dataset_pca/dataset_pca_results.npz,npz,2640628,b1708aba1a114e73a51a646fa57e847c91937ffe60860725bd4f36571d71b4e9,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch14_dinov2/sep/sep_results.npz,npz,14709,7c3dee387c6702e2c1370d7e0470a3710c35884120bfecb47dcb2c4b24e42b65,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch14_dinov2/sep/sep_thresholds.csv,csv,263,8bd05a3dd63c0376adae6992e32f65a7e166e935c724760a65bb410345a80a5d,datasets.load_dataset or pandas.read_csv
+raw/vit_base_patch14_dinov2/svd/svd_results.npz,npz,42654,0fcab37df22be0d7ba98bb058e6ff6958a1d763ca9e6867112b490d0ed431470,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_224_dino/dataset_pca/dataset_pca_results.npz,npz,2630959,70d9e15f740d5af2c69bd2a7ebb3f2c2a0cd7e0b752b2a7d2eccda050038f6c0,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_224_dino/sep/sep_results.npz,npz,14701,f15850d92d2e132dbc9faa04c11b413a93efd6fe6d291cc7082d886aaf99ff48,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_224_dino/sep/sep_thresholds.csv,csv,253,959244d7ab7ca79d2d9dcc4c18cf27ce1158b63767548965871bf4674d82a25d,datasets.load_dataset or pandas.read_csv
+raw/vit_base_patch16_224_dino/svd/svd_results.npz,npz,42654,1827e2bb0ae1eb5091ca393fcf77a3bd80ec3e6ef3bda0a7a5bf976978e1169d,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_224_mae/dataset_pca/dataset_pca_results.npz,npz,2428697,623e32f3c86c5cc5e189ea4f4d599c381ff44428acbcbca95c800c7a53ab147f,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_224_mae/sep/sep_results.npz,npz,14697,54d6c35c3aa8f90f4e20356d9b70cd0fce977474ed641d5de1ab8876db094f12,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_224_mae/sep/sep_thresholds.csv,csv,248,6794e9e79799b247a525de274a814bc4b2590598a4bdd9e3b442cf8928780776,datasets.load_dataset or pandas.read_csv
+raw/vit_base_patch16_224_mae/svd/svd_results.npz,npz,42654,88c59373354855e5e88323f4d05c7f3e998ecafc9838a76d6017d9292960a473,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_clip_openai/dataset_pca/dataset_pca_results.npz,npz,2518772,4aa600a90d3dc9aecb858753682eef2722ab0ddfa032f8173bc64a8f27b6566f,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_clip_openai/sep/sep_results.npz,npz,14733,b0066717e083af366a3f07194d916a9f440106501bcd4853cf9d45b93ebe0683,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_base_patch16_clip_openai/sep/sep_thresholds.csv,csv,303,0c2183bafcc936608d7eaab58c61bc13cdf4578db1584f3a3d2e45b545e8cdfb,datasets.load_dataset or pandas.read_csv
+raw/vit_base_patch16_clip_openai/svd/svd_results.npz,npz,42654,9fd6b17ed0ff958661611f8dabb93e601218cd3efba06c195750756f06c26a9e,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_huge_patch14_224_mae/dataset_pca/dataset_pca_results.npz,npz,4051217,7e6e2ac798a9b28ca625878b2ae4bbb69d7d95bfc1bc417dab4ef6ed76622949,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_huge_patch14_224_mae/sep/sep_results.npz,npz,12625,d72bdf42ff717ca136e3b80843e5ca966db8a23304899f324c683ac7f2157151,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_huge_patch14_224_mae/sep/sep_thresholds.csv,csv,225,e6ca450b9756ec3c602955ffc7c354efdb41b94ab1ac8fef389ae8ba335f3eab,datasets.load_dataset or pandas.read_csv
+raw/vit_huge_patch14_224_mae/svd/svd_results.npz,npz,42654,b6230905a35080dfcd4bcc86628bc7265aab4ae5e3c132f76ec2dc67e8ec435e,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_21k_in1k/dataset_pca/dataset_pca_results.npz,npz,3363574,6b90983d1d2a2797e5b85338b86b17e5f1acc4c11234aa266bfa991c3a2a000a,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_21k_in1k/sep/sep_results.npz,npz,10581,52e3dd818c5a7532a2694038c7ee722a4a758a6b8d6160f5fe029824daa78223,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_21k_in1k/sep/sep_thresholds.csv,csv,228,f501c9c642915f1b32bb36e1244de3edd83723ee73d8e931976451ada5287e02,datasets.load_dataset or pandas.read_csv
+raw/vit_large_21k_in1k/svd/svd_results.npz,npz,42654,2879b7fb01fa3d2955b648c83ba95bfaf7d5afc241883890ccb5ef4f12e91530,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch14_clip_openai/dataset_pca/dataset_pca_results.npz,npz,3373103,46a7b6a0a721a8674f9ad6361c0015142633261c05d458f5bfe7a973f9c0ce4d,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch14_clip_openai/sep/sep_results.npz,npz,18833,dce714ff28f3f5392afd6706443dbb2012ab7b903a6cf377e26926320e73a31e,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch14_clip_openai/sep/sep_thresholds.csv,csv,313,f3e595e0ee3fe86e6fc514cdde3b5a6624022763affb67467aaf63631ee5c8dd,datasets.load_dataset or pandas.read_csv
+raw/vit_large_patch14_clip_openai/svd/svd_results.npz,npz,42654,f5785d9252c4d787a293ab5efae80f2c6c559d135d69a6559e93c9a197e168cd,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch14_dinov2/dataset_pca/dataset_pca_results.npz,npz,3525854,8b738f0c6b0af186531822147e06cc17039c5653f120fa8682c48b84b85d5cae,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch14_dinov2/sep/sep_results.npz,npz,18809,6a9895c560f0c6d93594864f2906b7fe48f7f273b59aa74552019591b997ab0f,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch14_dinov2/sep/sep_thresholds.csv,csv,273,8d1406a019341b249e6e7cfcaafe8c6353d9dcc7637a75dac19e33a6483c9fbc,datasets.load_dataset or pandas.read_csv
+raw/vit_large_patch14_dinov2/svd/svd_results.npz,npz,42654,9992f47eb72eaeeede4d3749940e83495da3a2794d9baca4ce9803cd54c02985,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch16_224_mae/dataset_pca/dataset_pca_results.npz,npz,3214625,73e1f29d696400076a1ddc6f45c9e0aed65e0a2a45d585892c0d078604c7436b,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch16_224_mae/sep/sep_results.npz,npz,18797,a7db849019b02dbd71d824b9310d645cfd820defd26ec73a0479a4cf370eb46e,hf_hub_download + numpy.load(allow_pickle=False)
+raw/vit_large_patch16_224_mae/sep/sep_thresholds.csv,csv,258,fd2ea9877c2a3096978e347e72e210f3e51453295dca65d09e43ce099b82ff5a,datasets.load_dataset or pandas.read_csv
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diff --git a/data/npz_array_catalog.csv b/data/npz_array_catalog.csv
new file mode 100644
index 0000000000000000000000000000000000000000..42d6b08e8ed3d3b9e5493590116055805731afd6
--- /dev/null
+++ b/data/npz_array_catalog.csv
@@ -0,0 +1,337 @@
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+Model,Dimension (D),Num Tokens,d/D @ 50%,d @ 50%,d/D @ 60%,d @ 60%,d/D @ 70%,d @ 70%,d/D @ 80%,d @ 80%,d/D @ 90%,d @ 90%
+ViT-Tiny,192,196000,0.505208,97,0.604167,116,0.703125,135,0.796875,153,0.901042,173
+CaiT-S24,384,196000,0.500000,192,0.598958,230,0.703125,270,0.804688,309,0.901042,346
+DeiT-Small,384,196000,0.502604,193,0.601562,231,0.700521,269,0.796875,306,0.901042,346
+ViT-Large,1024,196000,0.500000,512,0.598633,613,0.700195,717,0.800781,820,0.899414,921
+ViT-Huge,1280,256000,0.493750,632,0.592969,759,0.694531,889,0.800000,1024,0.900781,1153
+Swin-Small,768,49000,0.501302,385,0.598958,460,0.700521,538,0.799479,614,0.901042,692
+"ViT-Base (CLIP, OpenAI)",768,196000,0.500000,384,0.596354,458,0.697917,536,0.804688,618,0.901042,692
+"ViT-Large (CLIP, OpenAI)",1024,256000,0.499023,511,0.601562,616,0.696289,713,0.801758,821,0.900391,922
+ViT-Base (DINOv2),768,256000,0.500000,384,0.600260,461,0.701823,539,0.802083,616,0.899740,691
+ViT-Large (DINOv2),1024,256000,0.497070,509,0.599609,614,0.699219,716,0.798828,818,0.899414,921
+ViT-Base (DINO),768,196000,0.496094,381,0.597656,459,0.700521,538,0.798177,613,0.901042,692
+ViT-Small (DINO),384,196000,0.476562,183,0.583333,224,0.690104,265,0.791667,304,0.895833,344
+ViT-Base (MAE),768,196,0.493490,379,0.588542,452,0.684896,526,0.789062,606,0.895833,688
+ViT-Large (MAE),1024,196000,0.500000,512,0.596680,611,0.697266,714,0.797852,817,0.900391,922
diff --git a/raw/comparison/svd_pca/svd_rank_table.md b/raw/comparison/svd_pca/svd_rank_table.md
new file mode 100644
index 0000000000000000000000000000000000000000..9d5038afba596aa7fdccbcfa09df408d8f43b672
--- /dev/null
+++ b/raw/comparison/svd_pca/svd_rank_table.md
@@ -0,0 +1,16 @@
+| Train Method | Model | Dims | SVD 80% | SVD 90% | SVD 95% | SVD 99% | Dataset 80% | Dataset 90% | Dataset 95% | Dataset 99% |
+|:--|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|
+| SL | ViT-Tiny | 192 | 1 | 2 | 7 | 52 | 1 | 17 | 71 | 154 |
+| SL | CaiT-S24 | 384 | 14 | 34 | 61 | 121 | 167 | 247 | 302 | 359 |
+| SL | DeiT-Small | 384 | 30 | 59 | 89 | 146 | 205 | 278 | 324 | 369 |
+| SL | Swin-Small | 768 | 3 | 7 | 15 | 33 | 129 | 336 | 495 | 691 |
+| SL | ViT-Large | 1024 | 5 | 16 | 39 | 115 | 255 | 498 | 694 | 933 |
+| SSL | ViT-Huge (MAE) | 1280 | 1 | 15 | 58 | 164 | 1 | 83 | 371 | 977 |
+| SSL | ViT-Base (DINOv2) | 768 | 19 | 39 | 67 | 146 | 384 | 519 | 606 | 704 |
+| SSL | ViT-Large (DINOv2) | 1024 | 30 | 58 | 93 | 173 | 525 | 696 | 813 | 940 |
+| SSL | ViT-Base (DINO) | 768 | 39 | 75 | 109 | 165 | 382 | 529 | 624 | 731 |
+| SSL | ViT-Small (DINO) | 384 | 41 | 72 | 101 | 152 | 232 | 297 | 336 | 373 |
+| SSL | ViT-Base (MAE) | 768 | 1 | 7 | 34 | 113 | 1 | 58 | 235 | 585 |
+| SSL | ViT-Large (MAE) | 1024 | 1 | 9 | 38 | 120 | 1 | 49 | 268 | 765 |
+| MM | ViT-Base (CLIP, OpenAI) | 768 | 6 | 27 | 60 | 135 | 129 | 340 | 502 | 704 |
+| MM | ViT-Large (CLIP, OpenAI) | 1024 | 15 | 46 | 89 | 181 | 230 | 506 | 713 | 945 |
\ No newline at end of file
diff --git a/raw/comparison/svd_pca/svd_rank_table.tex b/raw/comparison/svd_pca/svd_rank_table.tex
new file mode 100644
index 0000000000000000000000000000000000000000..f66036cf9e48de3349230f2db87a796a56d54109
--- /dev/null
+++ b/raw/comparison/svd_pca/svd_rank_table.tex
@@ -0,0 +1,24 @@
+\begin{tabular}{l l r r r r r r r r r}
+\toprule
+Train Method & Model & Dims & \multicolumn{4}{c}{Per-image SVD (99th pct)} & \multicolumn{4}{c}{Dataset PCA (shared, 99th pct)} \\
+\cmidrule(lr){4-7}\cmidrule(lr){8-11}
+ & & & 80\% & 90\% & 95\% & 99\% & 80\% & 90\% & 95\% & 99\% \\
+\midrule
+SL & ViT-Tiny & 192 & 1 & 2 & 7 & 52 & 1 & 17 & 71 & 154 \\
+SL & CaiT-S24 & 384 & 14 & 34 & 61 & 121 & 167 & 247 & 302 & 359 \\
+SL & DeiT-Small & 384 & 30 & 59 & 89 & 146 & 205 & 278 & 324 & 369 \\
+SL & Swin-Small & 768 & 3 & 7 & 15 & 33 & 129 & 336 & 495 & 691 \\
+SL & ViT-Large & 1024 & 5 & 16 & 39 & 115 & 255 & 498 & 694 & 933 \\
+\midrule
+SSL & ViT-Huge (MAE) & 1280 & 1 & 15 & 58 & 164 & 1 & 83 & 371 & 977 \\
+SSL & ViT-Base (DINOv2) & 768 & 19 & 39 & 67 & 146 & 384 & 519 & 606 & 704 \\
+SSL & ViT-Large (DINOv2) & 1024 & 30 & 58 & 93 & 173 & 525 & 696 & 813 & 940 \\
+SSL & ViT-Base (DINO) & 768 & 39 & 75 & 109 & 165 & 382 & 529 & 624 & 731 \\
+SSL & ViT-Small (DINO) & 384 & 41 & 72 & 101 & 152 & 232 & 297 & 336 & 373 \\
+SSL & ViT-Base (MAE) & 768 & 1 & 7 & 34 & 113 & 1 & 58 & 235 & 585 \\
+SSL & ViT-Large (MAE) & 1024 & 1 & 9 & 38 & 120 & 1 & 49 & 268 & 765 \\
+\midrule
+MM & ViT-Base (CLIP, OpenAI) & 768 & 6 & 27 & 60 & 135 & 129 & 340 & 502 & 704 \\
+MM & ViT-Large (CLIP, OpenAI) & 1024 & 15 & 46 & 89 & 181 & 230 & 506 & 713 & 945 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
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