In a deep neural network (e.g., a CNN for image classification), hidden layers often learn a feature hierarchy. Which statement best describes what this means?

Question

BlackTom AI · Accepted Answer

Earlier convolutional layers detect localized low-level features (edges, color blobs, simple textures); deeper layers compose those into parts and full-object concepts.

1. Relevant knowledge points and concepts
- Convolutional Neural Networks (CNNs) use stacked layers of convolutions, nonlinearities, and pooling to transform raw pixels into higher-level representations. 
- Receptive field: each neuron in deeper layers “sees” a larger region of the input, enabling combination of lower-level patterns. 
- Hierarchical feature learning: layers progressively extract features of increasing abstraction. 
- Empirical evidence: filter visualizations, activation maximization, and transfer learning show early layers learn generic features while later layers are task-specific.

2. Step-by-step reasoning
- Convolutional filters in the first layers learn to detect simple primitives (horizontal/vertical edges, gradients, color blobs) because these are the most useful low-level regularities for many tasks.
- Pooling and deeper convolutions aggregate these primitives over larger spatial extents; neurons begin to respond to combinations of edges (corners, textures) and then to arrangements forming parts (eyes, wheels).
- Deeper still, fully connected or high-level convolutional units respond to combinations of parts, corresponding to object-level concepts.
- Backpropagation encourages this organized decomposition because composing simple features to explain more complex structure is efficient for minimizing loss.

3. Calculations
- Not applicable; reasoning is qualitative and empirical rather than arithmetic.

4. Why the correct answer is correct
- It directly matches the theoretical and empirical understanding of CNNs: early layers capture general, local structure; deeper layers capture complex, task-specific combinations and semantics.

5. Why other options are incorrect
- “Hidden layers do not perform feature extraction”: false — hidden layers are the primary learned feature extractors.
- “Each hidden layer learns the same features”: false — depth changes receptive field and abstraction; layers specialize differently.
- “Feature extraction only via manual engineering”: false — learned weights in deep nets perform feature extraction automatically.

6. Conclusion
The correct choice is: Earlier layers learn simple patterns (e.g., edges/textures), while deeper layers combine them into higher-level concepts (e.g., parts/objects), which matches the provided answer.

In a deep neural network (e.g., a CNN for image classification), hidden layers often learn a feature hierarchy. Which statement best describes what this means?单项选择题

类似问题

How can deep convolutional neural network (DCNN) face recognition models be more like human face processing?

在 2D 卷积神经网络（CNN）中，卷积核的数量说明了什么？ In a 2-D convolutional neural network (CNN), what does the number of kernels define?

Which statement best describes what early CNN layers tend to learn?

Which of the following Neural Networks is used in Machine Vision system?

哪一项陈述正确描述了卷积神经网络 (CNN) 在识别中的特征？ Which statement describes a characteristic of the Convolutional Neural Network (CNN) in recognition correctly?

This is the CNN Architecture diagram that was explained in lectures. Match A, B, C, D, and E to the correct labels below. Note: Full marks will only be awarded for getting all matches correct here. You must make selections in all drop down lists in order for this question to be counted.

In implementing CNNs, we actually implement the forwards pass as a cross-correlation. This means that for the backward pass, the gradient of the loss with respect to the input is

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单