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Found 8 papers, 2 papers with code
Zero-shot Domain Adaptation Based on Attribute Information
In this paper, we propose a novel domain adaptation method that can be applied without target data.
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics
In this setting, we cannot access source data during adaptation, while unlabeled target data and a model pretrained with source data are given.
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools.
Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision
Transformer architectures have brought about fundamental changes to computational linguistic field, which had been dominated by recurrent neural networks for many years.
Semi-supervised learning by selective training with pseudo labels via confidence estimation
Since accurate estimation of the confidence is crucial in our method, we also propose a new data augmentation method, called MixConf, that enables us to obtain confidence-calibrated models even when the number of training data is small.
Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models
Our goal is to develop fine-grained real-image editing methods suitable for real-world applications.
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter
We address the challenge of training a large supernet for the object detection task, using a relatively small amount of training data.
Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion
We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D.
