59 citations · 90 across the 5 of their papers we have counts for
5 papers
Distribution Shift Inversion for Out-of-Distribution Prediction
Runpeng Yu, Songhua Liu, Xingyi Yang +1
Machine learning society has witnessed the emergence of a myriad of Out-of-Distribution (OoD) algorithms, which address the distribution shift between the training and the testing…
Deep Model Reassembly
Xingyi Yang, Daquan Zhou, Songhua Liu +2
In this paper, we explore a novel knowledge-transfer task, termed as Deep Model Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous models pre-t…
Dataset Distillation via Factorization
Songhua Liu, Kai Wang, Xingyi Yang +2
In this paper, we study \xw{dataset distillation (DD)}, from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play…
Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
Songhua Liu, Tianwei Lin, Dongliang He +5
Neural painting refers to the procedure of producing a series of strokes for a given image and non-photo-realistically recreating it using neural networks. While reinforcement lear…
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Songhua Liu, Tianwei Lin, Dongliang He +6
Fast arbitrary neural style transfer has attracted widespread attention from academic, industrial and art communities due to its flexibility in enabling various applications. Exist…