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cs.CV2026
ExFusion: Efficient Transformer Training via Multi-Experts Fusion
Jiacheng Ruan, Daize Dong, Xiaoye Qu +5
Mixture-of-Experts (MoE) models substantially improve performance by increasing the capacity of dense architectures. However, directly training MoE models requires considerable com…
cs.CV2024
iDAT: inverse Distillation Adapter-Tuning
Jiacheng Ruan, Jingsheng Gao, Mingye Xie +4
Adapter-Tuning (AT) method involves freezing a pre-trained model and introducing trainable adapter modules to acquire downstream knowledge, thereby calibrating the model for better…
cs.CV2022
SD-Conv: Towards the Parameter-Efficiency of Dynamic Convolution
Shwai He, Chenbo Jiang, Daize Dong +1
Dynamic convolution achieves better performance for efficient CNNs at the cost of negligible FLOPs increase. However, the performance increase can not match the significantly expan…