Robustify Transformers with Robust Kernel Density Estimation

Xing Han, Tongzheng Ren, Tan Minh Nguyen, Khai Nguyen, Joydeep Ghosh, Nhat Ho

Keywords: Transformers, Kernel Density Estimation, Robustness

Abstract: Recent advances in Transformer architecture have empowered its empirical success in various tasks across different domains. However, existing works mainly focus on improving the standard accuracy and computational cost, without considering the robustness of contaminated samples. Existing work (Nguyen et al, 2022, FourierFormer) has shown that the self-attention mechanism, which is the center of the Transformer architecture, can be viewed as a non-parametric estimator based on the well-known kernel density estimation (KDE). This motivates us to leverage the robust kernel density estimation (RKDE) in the self-attention mechanism, to alleviate the issue of the contamination of data by down-weighting the weight of bad samples in the estimation process. The modified self-attention mechanism can be incorporated into different Transformer variants. Empirical results on language modeling and image classification tasks demonstrate the effectiveness of this approach.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning

TL;DR: We propose a robust transformer that can that can alleviate the effect from contaminated data while improve the clean data performance.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/robustify-transformers-with-robust-kernel/code)