59 citations · 104 across the 12 of their papers we have counts for
12 papers
SG-Former: Self-guided Transformer with Evolving Token Reallocation
Sucheng Ren, Xingyi Yang, Songhua Liu +1
Vision Transformer has demonstrated impressive success across various vision tasks. However, its heavy computation cost, which grows quadratically with respect to the token sequenc…
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…
Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer
Hao Tang, Songhua Liu, Tianwei Lin +4
Transformer-based models achieve favorable performance in artistic style transfer recently thanks to its global receptive field and powerful multi-head/layer attention operations.…
Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate
Songhua Liu, Jingwen Ye, Xinchao Wang
Style transfer aims to render the style of a given image for style reference to another given image for content reference, and has been widely adopted in artistic generation and im…
Partial Network Cloning
Jingwen Ye, Songhua Liu, Xinchao Wang
In this paper, we study a novel task that enables partial knowledge transfer from pre-trained models, which we term as Partial Network Cloning (PNC). Unlike prior methods that upda…
Dataset Distillation: A Comprehensive Review
Ruonan Yu, Songhua Liu, Xinchao Wang
Recent success of deep learning is largely attributed to the sheer amount of data used for training deep neural networks.Despite the unprecedented success, the massive data, unfort…
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…
Learning with Recoverable Forgetting
Jingwen Ye, Yifang Fu, Jie Song +5
Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge. However, the involved training data may not be life-long legitimate du…
DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
Songhua Liu, Jingwen Ye, Sucheng Ren +1
One key challenge of exemplar-guided image generation lies in establishing fine-grained correspondences between input and guided images. Prior approaches, despite the promising res…
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…