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ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied to Vision Transformers
Machine unlearning (MUL) is an arising field in machine learning that seeks to erase the learned information of specific training data points from a trained model.
Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization
With growing concerns surrounding privacy and regulatory compliance, the concept of machine unlearning has gained prominence, aiming to selectively forget or erase specific learned information from a trained model.
PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting
PCT-CycleGAN generates temporal causality using two generator networks with forward and backward temporal dynamics in paired complementary cycles.
Pea-KD: Parameter-efficient and Accurate Knowledge Distillation on BERT
PTP is a KD-specialized initialization method, which can act as a good initial guide for the student.
Pea-KD: Parameter-efficient and accurate Knowledge Distillation
SPS is a new parameter sharing method that allows greater model complexity for the student model.
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