@ NeurIPS 2023, CPAL 2024, ICLR 2024

**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:

- Our NeurIPS 2023 paper, which developed the original CRATE framework and applied it to vision classification.
- Our CPAL 2024 paper, which used the learned representations in classification-trained CRATE models for semantic segmentation tasks.
- Our ICLR 2024 paper, which extended the CRATE framework to self-supervised learning tasks, including (masked) autoencoding.

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.

**CRATE** (**C**oding **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

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:**

- in each attention head, the \(\boldsymbol{Q}\), \(\boldsymbol{K}\), and \(\boldsymbol{V}\) weight matrices are weight-tied, i.e., set to be equal;
- and the nonlinearity following each attention layer is no longer a multi-layer perceptron (MLP), but rather a more structured operator (\(\texttt{ISTA}\)) with sparse outputs.

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**.

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.

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.

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

- "Token Statistics Transformer: Linear Time Attention via Variational Rate Reduction," which proposes a new compression objective within the CRATE framework such that the resulting Token Statistics Transformer (ToST) model has a linear-time attention mechanism which is performant as well as principled and interpretable.
- "Scaling White-Box Transformers for Vision" [project website, paper, code], which proposes a new sparsification mechanism within the CRATE framework such that the resulting CRATE-$\alpha$ model can scale to very large datasets and architectures while remaining mathematically and conventionally interpretable.