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
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 File: Rinhee_2019-07.zip ...
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. Instead of training a model from scratch, you
Compress the data to make it easier for a machine to store and search. File: Rinhee_2019-07.zip ...
Turn multi-dimensional data into a single long list of numbers.