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Found 13 papers, 6 papers with code
Denoising Task Difficulty-based Curriculum for Training Diffusion Models
By organizing timesteps or noise levels into clusters and training models with descending orders of difficulty, we facilitate an order-aware training regime, progressing from easier to harder denoising tasks, thereby deviating from the conventional approach of training diffusion models simultaneously across all timesteps.
Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
To achieve this, we employ a sparse mixture-of-experts within each transformer block to utilize semantic information and facilitate handling conflicts in tasks through parameter isolation.
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D
This work introduces HarmonyView, a simple yet effective diffusion sampling technique adept at decomposing two intricate aspects in single-image 3D generation: consistency and diversity.
Denoising Task Routing for Diffusion Models
Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL).
Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach
Indeed, when naively training the diagnostic model using CAT response data, we observe that item profiles deviate significantly from the ground-truth.
Multi-Architecture Multi-Expert Diffusion Models
In this paper, we address the performance degradation of efficient diffusion models by introducing Multi-architecturE Multi-Expert diffusion models (MEME).
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function
Self-supervised contrastive learning (CL) has achieved state-of-the-art performance in representation learning by minimizing the distance between positive pairs while maximizing that of negative ones.
Towards Practical Plug-and-Play Diffusion Models
For that, the existing practice is to fine-tune the guidance models with labeled data corrupted with noises.
Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI
In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI.
Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution
Although deep convolutional neural networks (CNNs) have obtained outstanding performance in image superresolution (SR), their computational cost increases geometrically as CNN models get deeper and wider.
Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations
The DC and cDC for automatic STN segmentation were 0. 66 and 0. 80, respectively.
Learning Latent Semantic Representation from Pre-defined Generative Model
Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables that follow the distribution.
Inception-Residual Block based Neural Network for Thermal Image Denoising
The proposed approach is tested for thermal images.
