The DELTA framework, designed for image classification tasks in long-tailed online continual learning scenarios, can be extended to handle other types of data modalities, such as text or audio, by considering the following adaptations:
Feature Extraction: Modify the feature extraction components of the framework to accommodate the unique characteristics of text or audio data. For text data, techniques like word embeddings or language models can be integrated, while for audio data, spectrogram representations or audio embeddings can be utilized.
Task-Specific Representations: Develop task-specific representations for text or audio data that capture the underlying patterns and semantics relevant to the continual learning tasks. This may involve leveraging pre-trained models or domain-specific embeddings.
Loss Functions: Customize the loss functions used in the framework to suit the nature of text or audio data. For text data, techniques like contrastive loss with sentence embeddings can be explored, while for audio data, methods like triplet loss with audio embeddings can be considered.
Data Augmentation: Implement data augmentation techniques tailored for text or audio data to enhance the model's ability to learn from limited samples and mitigate overfitting. This may include techniques like word dropout for text or time-frequency transformations for audio.
Domain-Specific Challenges: Address domain-specific challenges related to text or audio data, such as handling sequential dependencies in text data or capturing temporal dynamics in audio data. Customized modules or architectures can be designed to tackle these challenges effectively.
By adapting the DELTA framework to handle text or audio data modalities in long-tailed online continual learning scenarios, it can be applied to a wider range of real-world applications beyond image classification, showcasing its versatility and effectiveness across diverse data types.