Cong Wang

Pál Zsámboki, Benjamin Levi, David Ansel Josef Smith et al.

We study length generalization in transformers through the set complement task, where a model must predict a uniform distribution over tokens absent from an input sequence -- an ability central to board-game style reasoning. Our main theoretical result establishes two statements. First, we prove tight bounds on embedding and value dimensions for single-layer attention-only transformers. Second, we show that if such a model achieves balanced logit displacement at lengths 1 and 2, then it must generalize to longer sequences, though with reduced precision. A mechanistic reading of the proof explains this limitation: as more tokens are attended to, softmax compresses logit displacements, eroding separation between valid and invalid outputs. Training dynamics also suggest a second obstacle: when many next tokens are possible, updates become noisy. We hypothesize that dropout can counteract the first effect and Exponential Moving Average (EMA) the second. We validate these hypotheses through random hyperparameter search on the set complement task, which confirms both mechanisms. We then test OthelloGPT, a GPT-1 style model trained on random Othello moves, and find that EMA again improves length generalization in this more complex setting.

Cong Wang, Fan Tang, Yong Zhang et al.

Regional facial image synthesis conditioned on semantic mask has achieved great success using generative adversarial networks. However, the appearance of different regions may be inconsistent with each other when conducting regional image editing. In this paper, we focus on the problem of harmonized regional style transfer and manipulation for facial images. The proposed approach supports regional style transfer and manipulation at the same time. A multi-scale encoder and style mapping networks are proposed in our work. The encoder is responsible for extracting regional styles of real faces. Style mapping networks generate styles from random samples for all facial regions. As the key part of our work, we propose a multi-region style attention module to adapt the multiple regional style embeddings from a reference image to a target image for generating harmonious and plausible results. Furthermore, we propose a new metric "harmony score" and conduct experiments in a challenging setting: three widely used face datasets are involved and we test the model by transferring the regional facial appearance between datasets. Images in different datasets are usually quite different, which makes the inconsistency between target and reference regions more obvious. Results show that our model can generate reliable style transfer and multi-modal manipulation results compared with SOTAs. Furthermore, we show two face editing applications using the proposed approach.