1 citations · 4 across the 17 of their papers we have counts for
7 papers · 1 filter
Towards Better Performance in Incomplete LDL: Addressing Data Imbalance
Zhiqiang Kou, Haoyuan Xuan, Jing Wang +2
Label Distribution Learning (LDL) is a novel machine learning paradigm that addresses the problem of label ambiguity and has found widespread applications. Obtaining complete label…
Redefining <Creative> in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation
Fu Feng, Yucheng Xie, Xu Yang +2
``Creative'' remains an inherently abstract concept for both humans and diffusion models. While text-to-image (T2I) diffusion models can easily generate out-of-distribution concept…
FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models
Yucheng Xie, Fu Feng, Ruixiao Shi +4
The training of diffusion models is computationally intensive, making effective pre-training essential. However, real-world deployments often demand models of variable sizes due to…
KIND: Knowledge Integration and Diversion for Training Decomposable Models
Yucheng Xie, Fu Feng, Ruixiao Shi +3
Pre-trained models have become the preferred backbone due to the increasing complexity of model parameters. However, traditional pre-trained models often face deployment challenges…
WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models
Fu Feng, Yucheng Xie, Jing Wang +1
The growing complexity of model parameters underscores the significance of pre-trained models. However, deployment constraints often necessitate models of varying sizes, exposing l…
Inaccurate Label Distribution Learning with Dependency Noise
Zhiqiang Kou, Jing Wang, Yuheng Jia +1
In this paper, we introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learn…
Transferring Core Knowledge via Learngenes
Fu Feng, Jing Wang, Xin Geng
The pre-training paradigm fine-tunes the models trained on large-scale datasets to downstream tasks with enhanced performance. It transfers all knowledge to downstream tasks withou…