atrous

atrous的音标是["ætrəs]，基本翻译是“间隔的，延迟的”。速记技巧可以是将每个字母拆分，尝试将其与其它词进行联系，或者使用首字母组合记忆法。例如，可以将atrous中的字母分别对应单词“at”（在）、“troop”（队伍）中的部分字母，从而联想到“在队伍中存在间隔”这一场景，以此进行记忆。

atrous这个词的词源可以追溯到拉丁语中的“atros”，意为“黑暗的”或“深色的”。它的变化形式包括其过去式“atrous”和过去分词“atrous”，现在分词为“atrous”，形容词形式为“atrous或atrous”。

相关单词：

1. Atrocity - 意为“残暴的行为”，这个词源于拉丁语中的“atrocitas”，意为“野蛮、残忍”。

2. Depth - 意为“深度”，这个词源于拉丁语中的“atrocitas”和“profundus”，意为“深”或“深处”。

3. Darkness - 意为“黑暗”，这个词直接来源于atrous的词源，表示缺乏光明或完全的黑暗。

4. Atrax - 是一个古希腊语词，意为“黑暗的元素”，与atrous有相似的含义。

5. Atrophy - 意为“萎缩”，这个词源于拉丁语中的“atros”和“rumpere”，意为“变薄、变弱”。

6. Atrine - 是一个冰岛语词汇，意为“黑色的元素”，与atrous有相似的含义。

7. Atrocious - 意为“极其恶劣的”，这个词源于拉丁语中的“atrox”和“cuius”，意为“恶劣的”。

8. Atrophyd - 是一个合成词，由atrous和deformed的意思组合而成，表示畸形或萎缩的状态。

9. Atrineous - 是一个合成词，由atrous和stone的意思组合而成，表示黑色的石头。

10. Atroity - 是一个新造词，由atrous和名词后缀-ity组合而成，表示黑暗或野蛮的性质。

常用短语：

1. atrous convolution

2. atrous spatial pyramid pooling

3. atrous pooling

4. atrous spatial transformer network

5. atrous fully connected

6. atrous spatial downsampling

7. atrous spatial upsampling

双语例句：

1. This network uses atrous spatial pyramid pooling to capture contextual information across multiple scales.

2. The atrous spatial transformer network allows for precise spatial manipulation of the input data.

3. Atrous fully connected layers provide a more efficient way of training deep neural networks.

4. Atrous spatial downsampling allows for a more gradual reduction in resolution without significant loss of detail.

5. Atrous convolutional filters provide a way to increase the receptive field without increasing the number of parameters.

6. The atrous spatial upsampling technique can be used to increase the resolution of an image without significant loss of quality.

7. The combination of atrous pooling and convolutional neural networks provides a powerful tool for feature extraction and classification tasks.

英文小作文：

Atrous Convolutional Neural Networks: A Tool for Feature Extraction and Classification

In recent years, convolutional neural networks (CNNs) have become increasingly popular for a wide range of tasks, including image classification, object detection, and segmentation. One of the key components of CNNs is the convolutional filter, which is used to extract features from the input data. However, traditional convolutional filters have a fixed size, which limits their ability to extract features from different scales and regions of the input data. This limitation can be overcome by using atrous convolutional filters, which allow for an increase in the receptive field without increasing the number of parameters.

Atrous convolutional filters provide a way to extract features from different scales and regions of the input data, which can be beneficial for tasks such as object detection and segmentation, where it is important to recognize objects of different sizes and locations. By using atrous convolutional filters, CNNs can better capture contextual information across multiple scales and provide more accurate results. Additionally, atrous pooling techniques can be used in conjunction with CNNs to further enhance feature extraction and classification performance. By combining these techniques, CNNs can be effectively applied to a wide range of tasks, providing better results than traditional CNNs with fixed-size filters.