3 citations · 10 across the 14 of their papers we have counts for
7 papers · 1 filter
Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning
Haodong Lu, Chongyang Zhao, Minhui Xue +3
The central tension in continual learning (CL) is the trade-off between plasticity (acquiring new knowledge) and stability (retaining prior knowledge). We study how a pre-trained b…
Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models
Saurav Jha, Shiqi Yang, Masato Ishii +7
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However,…
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
Saurav Jha, Dong Gong, Lina Yao
Continual learning (CL) aims to help deep neural networks learn new knowledge while retaining what has been learned. Owing to their powerful generalizability, pre-trained vision-la…
Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
Huiyi Wang, Haodong Lu, Lina Yao +1
Continual learning (CL) aims to continually accumulate knowledge from a non-stationary data stream without catastrophic forgetting of learned knowledge, requiring a balance between…
Learning with Mixture of Prototypes for Out-of-Distribution Detection
Haodong Lu, Dong Gong, Shuo Wang +3
Out-of-distribution (OOD) detection aims to detect testing samples far away from the in-distribution (ID) training data, which is crucial for the safe deployment of machine learnin…
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model
Xiangyu Zhang, Daijiao Liu, Hexin Liu +5
Recently, Denoising Diffusion Probabilistic Models (DDPMs) have attained leading performances across a diverse range of generative tasks. However, in the field of speech synthesis,…
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks
Enbo He, Yitong Hao, Yue Zhang +2
Anomaly detection on attributed networks aims to find the nodes whose behaviors are significantly different from other majority nodes. Generally, network data contains information…