Image Classification Using Averaged Feature Matrices for Incremental Learning

The patent segments the feature space by identifying representative feature vectors for each class and dividing the dataset into subsets associated with different classes. This segmentation allows the model to process and learn from different classes separately, reducing interference between old and new tasks while maintaining the ability to adapt to new data distributions.

The patent performs preliminary actions by pre-calculating representative feature vectors for each class before actual learning occurs. These representative vectors serve as anchors that guide the learning process, allowing the model to maintain knowledge of old classes while learning new tasks. The representative feature vectors are computed in advance and used to initialize or constrain the learning process.

The patent introduces representative feature vectors as intermediaries between the input images and the learning process. These representative vectors act as mediators that capture the essential characteristics of each class, allowing the model to learn from small datasets without overfitting. The intermediaries provide a stable reference that improves generalization performance even when training data is limited.

The patent changes the parameter representation by using representative feature vectors instead of raw pixel data or standard feature embeddings. This parameter transformation allows the model to work effectively with small datasets by focusing on the most discriminative features. The parameter changes enable the model to achieve better generalization with fewer training examples.

The patent performs preliminary computation of representative feature vectors for each class before continuous learning begins. These pre-computed vectors serve as persistent memory anchors that preserve knowledge of old tasks. During continuous learning, the model can incorporate new tasks while referring back to these stable representative vectors, preventing catastrophic forgetting of previously learned tasks.

The patent creates copies of essential task knowledge in the form of representative feature vectors that are stored and reused across different learning tasks. Instead of relying solely on the neural network weights to retain knowledge, the system copies critical class characteristics into compact representative vectors that can be repeatedly used to guide learning of both old and new tasks, preventing information loss.