Instead of training a model from scratch, you can use a high-performance network that already "understands" data. Popular choices include: ResNet, VGG-19, or EfficientNet. For Text: BERT or GPT-based transformers. 2. Perform Feature Extraction
You pass your data through the network but "cut off" the final classification layer (the part that says "this is a cat"). What remains is the from the preceding layers: Early layers capture simple things like edges and colors. File: Rinhee_2019-07.zip ...
Compress the data to make it easier for a machine to store and search. Instead of training a model from scratch, you
Use the feature to find similar items in a database (like Image Retrieval ) or as input for a different machine learning task. Why use Deep Features? Exploiting deep cross-semantic features for image retrieval Compress the data to make it easier for
capture complex concepts like faces, textures, or specific objects. 3. Process and Store the Result Once the model outputs the feature vector, you can: