White-Box Transformers via Sparse Rate Reduction: Compression is All There Is?

@ NeurIPS 2023, CPAL 2024, ICLR 2024
Yaodong Yu1
Sam Buchanan2
Druv Pai1
Tianzhe Chu1 3
Ziyang Wu1
Shengbang Tong1
Hao Bai4
Yuexiang Zhai1
Ben Haeffele5
Yi Ma1 6
1UC Berkeley   2TTIC   3ShanghaiTech   4UIUC   5JHU   6HKU  


CRATE is a transformer-like architecture which is constructed through first principles, enjoys a rich theoretical framework, and achieves competitive performance across diverse training setups.

At the top of the page, we have linked a long-form manuscript explaining the CRATE architecture in full detail. Below, we summarize the numerous sub-projects that have developed the CRATE architecture. These consist of:

Code, paper links, bibliographic information, and short write-ups are listed below for each project. At the bottom of the page, we also shout-out some follow-up work.

White-Box Transformers via Sparse Rate Reduction

@ NeurIPS 2023

What is CRATE?

CRATE (Coding RATE transformer) is a white-box (mathematically interpretable) transformer architecture, where each layer performs a single step of an alternating minimization algorithm to optimize the sparse rate reduction objective

\[\max_{f}\mathbb{E}_{\boldsymbol{Z} = f(\boldsymbol{X})}[\Delta R(\boldsymbol{Z} \mid \boldsymbol{U}_{[K]}) - \lambda \|\boldsymbol{Z}\|_{0}] = \max_{f}\mathbb{E}_{\boldsymbol{Z} = f(\boldsymbol{X})}[R(\boldsymbol{Z}) - R^{c}(\boldsymbol{Z} \mid \boldsymbol{U}_{[K]}) - \lambda \|\boldsymbol{Z}\|_{0}],\]
\[ \begin{aligned} &\max_{f}\mathbb{E}_{\boldsymbol{Z} = f(\boldsymbol{X})}[\Delta R(\boldsymbol{Z} \mid \boldsymbol{U}_{[K]}) - \lambda \|\boldsymbol{Z}\|_{0}] \\ =& \max_{f}\mathbb{E}_{\boldsymbol{Z} = f(\boldsymbol{X})}[R(\boldsymbol{Z}) - R^{c}(\boldsymbol{Z} \mid \boldsymbol{U}_{[K]}) - \lambda \|\boldsymbol{Z}\|_{0}], \end{aligned} \]

where \(R\) and \(R^{c}\) are measures of compression of the final token representations \(\boldsymbol{Z} = f(\boldsymbol{X})\) w.r.t. different codebooks, and the \(\ell^{0}\) norm promotes the sparsity of \(\boldsymbol{Z}\). Overall, the sparse rate reduction objective promotes compact and sparse representations.

The function \(f\) is defined as

\[f = f^{L} \circ f^{L - 1} \circ \cdots \circ f^{1} \circ f^{\mathrm{pre}},\]

where \(f^{\mathrm{pre}}\) is the pre-processing mapping, and \(f^{\ell}\) is the \(\ell^{\mathrm{th}}\)-layer forward mapping that transforms the token distribution to optimize the above sparse rate reduction objective incrementally.

In particular, \(f^{\ell}\) implements a step of a parameterized optimization algorithm on the sparse rate reduction, thus making the representations more compact and sparse.


The following figure presents an overview of the general CRATE architecture:

After encoding input data \(\boldsymbol{X}\) as a sequence of tokens \(\boldsymbol{Z}^1\), CRATE constructs a deep network that transforms the data to a canonical configuration of low-dimensional subspaces by successive compression against a local model for the distribution, generating \(\boldsymbol{Z}^{\ell+1/2}\), and sparsification against a global dictionary, generating \(\boldsymbol{Z}^{\ell+1}\). Repeatedly stacking these blocks and training the model parameters via backpropagation yields a powerful and interpretable representation of the data.

The full architecture is simply a concatenation of such layers, with some initial tokenizer and final task-specific architecture (i.e., a classification head). Overall, CRATE is similar to a transformer, with two differences:


Below, the classification pipeline for CRATE is depicted. It is virtually identical to the popular vision transformer.

We use soft-max cross entropy loss to train on the supervised image classification task. We obtain competitive performance with the usual vision transformer (ViT) trained on classification, with similar scaling behavior, including above 80% top-1 accuracy on ImageNet-1K with 25% of the parameters of ViT.

Emergence of Segmentation with Minimalistic White-Box Transformers

@ CPAL 2024

Segmentation and Object Detection

An interesting phenomenon of CRATE is that even when trained on supervised classification, it learns to segment the input images, with such segmentations being easily recoverable via attention maps, as in the following pipeline (similar to DINO).

Such segmentations were only previously seen in transformer-like architectures using a complex self-supervised training mechanism as in DINO, yet in CRATE, segmentation emerges as a byproduct of supervised classification training. In particular, the model does not obtain any a priori segmentation information at any time. Below, we show some example segmentations.

Another remarkable property is that attention heads in CRATE automatically carry semantic meaning, which implies that CRATE may have post-hoc interpretability for any classification it makes. Below, we visualize the output of some attention heads across several images and several animals, showing that some attention heads correspond to different parts of the animal, and this correspondence is consistent across different animals and different classes of animals.

Masked Autoencoding via Structured Diffusion with White-Box Transformers

@ ICLR 2024


We extend CRATE to autoencoding by using the following pipeline.

To derive the decoder architecture, we propose a novel framework of structured denoising-diffusion, which is analogous to the common (ordinary) denoising-diffusion framework popularly used for generative modelling of imagery data. Our framework relies on a quantitative connection between the compression operator and the score function (as used in denoising-diffusion models), as shown below:

The encoder and decoder are derived through discretizations of the structured denoising and diffusion processes, respectively. Importantly, the encoder derived from structured denoising is exactly the previously described CRATE architecture. The full encoder and decoder layers are given below.

This CRATE autoencoding architecture achieves competitive performance on the masked autoencoding task, as shown by the below examples.

In addition, it obtains the same emergent properties as the classification-trained CRATE, such as interpretable attention maps in the encoder.


This work is partially supported by the ONR grant N00014-22-1-2102, and the joint Simons Foundation-NSF DMS grant 2031899.

This website template was adapted from Brent Yi's project page for TILTED.

Follow-up Projects

Some recent works have built off of the CRATE methodology or philosophy. Here is a non-exhaustive list of such works (with permission).