CNN features for image retrieval
WHAT IT TESTS: transfer learning for features. OUTLINE: pass the image through a pretrained CNN and read activations from a late layer as a descriptor; deeper layers encode semantics, earlier layers encode texture.
WHAT IT TESTS: how to repurpose a classification network as a feature extractor. ANSWER OUTLINE: run the image through a network pretrained on a large dataset, drop the classification head, and take activations from a deep layer, often the last pooling or a fully connected layer, as a compact descriptor; pool and L2-normalize it. Deep layers capture high-level semantic content useful for retrieval, while earlier layers capture low-level texture. Compare descriptors by cosine or Euclidean distance.
Read the original → interview
- #transfer-learning
- #cnn-features
- #image-retrieval
- #vgg16
- #embeddings
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