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AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation
This paper describes a simple yet effective technique for refining a pretrained classifier network.
Robustness to distribution shifts of compressed networks for edge devices
It is important to investigate the robustness of compressed networks in two types of data distribution shifts: domain shifts and adversarial perturbations.
KronA: Parameter Efficient Tuning with Kronecker Adapter
We apply the proposed methods for fine-tuning T5 on the GLUE benchmark to show that incorporating the Kronecker-based modules can outperform state-of-the-art PET methods.
Kronecker Decomposition for GPT Compression
GPT is an auto-regressive Transformer-based pre-trained language model which has attracted a lot of attention in the natural language processing (NLP) domain due to its state-of-the-art performance in several downstream tasks.
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