论文标题
使用端到端训练有素的暹罗网络和小组卷积，预测Algonauts挑战中的人口神经活动
Predicting population neural activity in the Algonauts challenge using end-to-end trained Siamese networks and group convolutions
论文作者
论文摘要
Algonauts挑战是在视觉大脑区域衍生的表示差异矩阵（RDMS）的形式预测对象表示。我们使用暹罗网络和小组卷积的概念使用了定制的深度学习模型，以预测与一对图像相对应的神经距离。训练数据最好用最后一层计算的距离来解释。
The Algonauts challenge is about predicting the object representations in the form of Representational Dissimilarity Matrices (RDMS) derived from visual brain regions. We used a customized deep learning model using the concept of Siamese networks and group convolutions to predict neural distances corresponding to a pair of images. Training data was best explained by distances computed over the last layer.
