atrous的音標是["?tr?s]，基本翻譯是“間隔的，延遲的”。速記技巧可以是將每個字母拆分，嘗試將其與其它詞進行聯(lián)系，或者使用首字母組合記憶法。例如，可以將atrous中的字母分別對應單詞“at”（在）、“troop”（隊伍）中的部分字母，從而聯(lián)想到“在隊伍中存在間隔”這一場景，以此進行記憶。

atrous這個詞的詞源可以追溯到拉丁語中的“atros”，意為“黑暗的”或“深色的”。它的變化形式包括其過去式“atrous”和過去分詞“atrous”，現(xiàn)在分詞為“atrous”，形容詞形式為“atrous或atrous”。

相關單詞：

1. Atrocity - 意為“殘暴的行為”，這個詞源于拉丁語中的“atrocitas”，意為“野蠻、殘忍”。

2. Depth - 意為“深度”，這個詞源于拉丁語中的“atrocitas”和“profundus”，意為“深”或“深處”。

3. Darkness - 意為“黑暗”，這個詞直接來源于atrous的詞源，表示缺乏光明或完全的黑暗。

4. Atrax - 是一個古希臘語詞，意為“黑暗的元素”，與atrous有相似的含義。

5. Atrophy - 意為“萎縮”，這個詞源于拉丁語中的“atros”和“rumpere”，意為“變薄、變弱”。

6. Atrine - 是一個冰島語詞匯，意為“黑色的元素”，與atrous有相似的含義。

7. Atrocious - 意為“極其惡劣的”，這個詞源于拉丁語中的“atrox”和“cuius”，意為“惡劣的”。

8. Atrophyd - 是一個合成詞，由atrous和deformed的意思組合而成，表示畸形或萎縮的狀態(tài)。

9. Atrineous - 是一個合成詞，由atrous和stone的意思組合而成，表示黑色的石頭。

10. Atroity - 是一個新造詞，由atrous和名詞后綴-ity組合而成，表示黑暗或野蠻的性質。

常用短語：

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

雙語例句：

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.