Poster
in
Workshop: Fine-Tuning in Modern Machine Learning: Principles and Scalability
Improving Fine-Tuning with Latent Cluster Correction
Cédric Thanh
Abstract:
The formation of salient clusters in the latent spaces of a neural network(NN) during training strongly impacts its final accuracy on classificationtasks. This paper proposes a novel fine-tuning method that boostsperformance by improving the quality of these latent clusters, using theLouvain community detection algorithm and a specifically designed lossfunction. We present preliminary results that demonstrate that this processyields an appreciable accuracy gain for classical NN architecturesfine-tuned on the CIFAR100 dataset.
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