Differential diagnosis of dementia is challenging due to overlapping
symptoms, with structural magnetic resonance imaging (MRI) being the primary
method for diagnosis. Despite the clinical value of computer-aided differential
diagnosis, research has been limited, mainly due to the absence of public
datasets that contain diverse types of dementia. This leaves researchers with
small in-house datasets that are insufficient for training deep neural networks
(DNNs). Self-supervised learning shows promise for utilizing unlabeled MRI
scans in training, but small batch sizes for volumetric brain scans make its
application challenging. To address these issues, we propose Triplet Training
for differential diagnosis with limited target data. It consists of three key
stages: (i) self-supervised pre-training on unlabeled data with Barlow Twins,
(ii) self-distillation on task-related data, and (iii) fine-tuning on the
target dataset. Our approach significantly outperforms traditional training
strategies, achieving a balanced accuracy of 75.6%. We further provide insights
into the training process by visualizing changes in the latent space after each
step. Finally, we validate the robustness of Triplet Training in terms of its
individual components in a comprehensive ablation study. Our code is available
at this https URL

三元训练用于有限目标数据的不同诊断，包括自监督的预训练、自蒸馏和目标数据上的微调，可有效提高传统训练策略的性能，实现了 75.6％的平衡准确性。

从巴洛双胞胎到三胞胎训练：用有限数据区分痴呆症

From Barlow Twins to Triplet Training: Differentiating Dementia with  Limited Data

Recent advances in cross-lingual text-to-speech (TTS) made it possible to
synthesize speech in a language foreign to a monolingual speaker. However,
there is still a large gap between the pronunciation of generated cross-lingual
speech and that of native speakers in terms of naturalness and intelligibility.
In this paper, a triplet training scheme is proposed to enhance the
cross-lingual pronunciation by allowing previously unseen content and speaker
combinations to be seen during training. Proposed method introduces an extra
fine-tune stage with triplet loss during training, which efficiently draws the
pronunciation of the synthesized foreign speech closer to those from the native
anchor speaker, while preserving the non-native speaker's timbre. Experiments
are conducted based on a state-of-the-art baseline cross-lingual TTS system and
its enhanced variants. All the objective and subjective evaluations show the
proposed method brings significant improvement in both intelligibility and
naturalness of the synthesized cross-lingual speech.

本文提出了一种三元组训练方案，借助额外的微调阶段和三元组损失在跨语言语音合成中提高发音自然度和可懂度。

使用三元组训练方案提升跨语言语音合成

Improving Cross-lingual Speech Synthesis with Triplet Training Scheme

Deep hashing establishes efficient and effective image retrieval by
end-to-end learning of deep representations and hash codes from similarity
data. We present a compact coding solution, focusing on deep learning to
quantization approach that has shown superior performance over hashing
solutions for similarity retrieval. We propose Deep Triplet Quantization (DTQ),
a novel approach to learning deep quantization models from the similarity
triplets. To enable more effective triplet training, we design a new triplet
selection approach, Group Hard, that randomly selects hard triplets in each
image group. To generate compact binary codes, we further apply a triplet
quantization with weak orthogonality during triplet training. The quantization
loss reduces the codebook redundancy and enhances the quantizability of deep
representations through back-propagation. Extensive experiments demonstrate
that DTQ can generate high-quality and compact binary codes, which yields
state-of-the-art image retrieval performance on three benchmark datasets,
NUS-WIDE, CIFAR-10, and MS-COCO.

通过深度学习和哈希编码来建立高效、有效的图像检索的解决方案，我们提出了 Deep Triplet Quantization（DTQ）方法来学习深度量化模型，并在三个基准数据集 NUS-WIDE、CIFAR-10 和 MS-COCO 上获得了表现优秀的图像检索结果。