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The paper mentions that HyperMask has some limitations in terms of memory consumption due to the requirement for the hypernetwork's output layer to match the number of parameters in the target network. Are there any potential strategies or approaches to mitigate this memory consumption issue?
HYPERMASK: ADAPTIVE HYPERNETWORK-BASED MASKS FOR CONTINUAL LEARNING Anonymous authors Paper under double-blind review ABSTRACT Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. To overcome this problem, there exist many continual learning strategies....
IAkflJmNrC
What is the reference model for semantic calculation used in sec 3.3 and sec 4? If I am understanding correctly, in section 4, you are using the base sentence embedding model without further finetuning as the reference model. Is it the same in sec 3.3?
Polarity-Aware Semantic Retrieval with Fine-Tuned Sentence Embeddings Anonymous authors Paper under double-blind review Abstract This paper investigates the effectiveness of retrieving sentences with multiple objectives – polarity and similarity – by fine-tuning sentence-transformer models on augmented supervised da...