3 citations · 5 across the 25 of their papers we have counts for
6 papers · 1 filter
GMem: A Modular Approach for Ultra-Efficient Generative Models
Yi Tang, Peng Sun, Zhenglin Cheng +1
Recent studies indicate that the denoising process in deep generative diffusion models implicitly learns and memorizes semantic information from the data distribution. These findin…
Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detectors
Tao Lin, Lijia Yu, Gaojie Jin +3
In recent years, the study of adversarial robustness in object detection systems, particularly those based on deep neural networks (DNNs), has become a pivotal area of research. Tr…
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning
Haobo Song, Hao Zhao, Soumajit Majumder +1
Fine-tuning large pre-trained foundation models, such as the 175B GPT-3, has attracted more attention for downstream tasks recently. While parameter-efficient fine-tuning methods h…
GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost
Xinyi Shang, Peng Sun, Tao Lin
Recent advancements in dataset distillation have demonstrated the significant benefits of employing soft labels generated by pre-trained teacher models. In this paper, we introduce…
Efficiency for Free: Ideal Data Are Transportable Representations
Peng Sun, Yi Jiang, Tao Lin
Data, the seminal opportunity and challenge in modern machine learning, currently constrains the scalability of representation learning and impedes the pace of model evolution. In…
Switch EMA: A Free Lunch for Better Flatness and Sharpness
Siyuan Li, Zicheng Liu, Juanxi Tian +9
Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DN…