Search Results for author:
Found 9 papers, 8 papers with code
Backpropagation Path Search On Adversarial Transferability
Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim models deployed in the black-box situation.
On the Robustness of Latent Diffusion Models
Therefore, in this paper, we aim to analyze the robustness of latent diffusion models more thoroughly.
DiffUTE: Universal Text Editing Diffusion Model
Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information.
Mobile User Interface Element Detection Via Adaptively Prompt Tuning
Recent object detection approaches rely on pretrained vision-language models for image-text alignment.
AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network
Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes.
DiffusionInst: Diffusion Model for Instance Segmentation
Extensive experimental results on COCO and LVIS show that DiffusionInst achieves competitive performance compared to existing instance segmentation models with various backbones, such as ResNet and Swin Transformers.
Ranked #6 on
Instance Segmentation
on LVIS v1.0 val
A2: Efficient Automated Attacker for Boosting Adversarial Training
In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training.
MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees
Despite the success of deep learning in computer vision and natural language processing, Gradient Boosted Decision Tree (GBDT) is yet one of the most powerful tools for applications with tabular data such as e-commerce and FinTech.
DIFER: Differentiable Automated Feature Engineering
Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.
