This study explores the utilization of Dermatoscopic synthetic data generated
through stable diffusion models as a strategy for enhancing the robustness of
machine learning model training. Synthetic data generation plays a pivotal role
in mitigating challenges associated with limited labeled datasets, thereby
facilitating more effective model training. In this context, we aim to
incorporate enhanced data transformation techniques by extending the recent
success of few-shot learning and a small amount of data representation in
text-to-image latent diffusion models. The optimally tuned model is further
used for rendering high-quality skin lesion synthetic data with diverse and
realistic characteristics, providing a valuable supplement and diversity to the
existing training data. We investigate the impact of incorporating newly
generated synthetic data into the training pipeline of state-of-art machine
learning models, assessing its effectiveness in enhancing model performance and
generalization to unseen real-world data. Our experimental results demonstrate
the efficacy of the synthetic data generated through stable diffusion models
helps in improving the robustness and adaptability of end-to-end CNN and vision
transformer models on two different real-world skin lesion datasets.

研究探讨了通过稳定扩散模型生成的皮肤镜合成数据的利用，作为增强机器学习模型训练鲁棒性的策略。实验结果表明，通过稳定扩散模型生成的合成数据有助于提高卷积神经网络和视觉转换器模型的鲁棒性和适应性，对于两个不同的真实世界皮肤病变数据集，其效果显著。

Derm-T2IM: 使用稳定的扩散模型利用合成皮肤损伤数据，通过 ViT 和 CNN 提升皮肤疾病分类

Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion  Models for Enhanced Skin Disease Classification using ViT and CNN

We present Spatial LibriSpeech, a spatial audio dataset with over 650 hours
of 19-channel audio, first-order ambisonics, and optional distractor noise.
Spatial LibriSpeech is designed for machine learning model training, and it
includes labels for source position, speaking direction, room acoustics and
geometry. Spatial LibriSpeech is generated by augmenting LibriSpeech samples
with 200k+ simulated acoustic conditions across 8k+ synthetic rooms. To
demonstrate the utility of our dataset, we train models on four spatial audio
tasks, resulting in a median absolute error of 6.60{\deg} on 3D source
localization, 0.43m on distance, 90.66ms on T30, and 2.74dB on DRR estimation.
We show that the same models generalize well to widely-used evaluation
datasets, e.g., obtaining a median absolute error of 12.43{\deg} on 3D source
localization on TUT Sound Events 2018, and 157.32ms on T30 estimation on ACE
Challenge.

我们提出了 Spatial LibriSpeech，这是一个具有超过 650 小时 19 通道音频、一阶 ambiSonics 和可选分心噪声的空间音频数据集。Spatial LibriSpeech 旨在用于机器学习模型训练，并包含源位置、说话方向、房间声学和几何标签。我们通过增加 LibriSpeech 样本与 8k + 合成房间中的 200k + 模拟声学条件来生成 Spatial LibriSpeech。为了证明我们数据集的实用性，我们对四个空间音频任务进行模型训练，结果 3D 源定位中的中值绝对误差为 6.60°、距离为 0.43m、T30 为 90.66ms，DRR 估计为 2.74dB。我们展示了相同模型在广泛使用的评估数据集上具有良好的泛化能力，例如在 TUT 声事件 2018 的 3D 源定位中的中值绝对误差为 12.43°，ACE 挑战中 T30 估计为 157.32ms。