Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

CycleCluster: Modernising Clustering Regularisation for Deep Semi-Supervised Classification (2001.05317v2)

Published 15 Jan 2020 in cs.LG and stat.ML

Abstract: Given the potential difficulties in obtaining large quantities of labelled data, many works have explored the use of deep semi-supervised learning, which uses both labelled and unlabelled data to train a neural network architecture. The vast majority of SSL approaches focus on implementing the low-density separation assumption or consistency assumption, the idea that decision boundaries should lie in low density regions. However, they have implemented this assumption by making local changes to the decision boundary at each data point, ignoring the global structure of the data. In this work, we explore an alternative approach using the global information present in the clustered data to update our decision boundaries. We propose a novel framework, CycleCluster, for deep semi-supervised classification. Our core optimisation is driven by a new clustering based regularisation along with a graph based pseudo-labels and a shared deep network. Demonstrating that direct implementation of the cluster assumption is a viable alternative to the popular consistency based regularisation. We demonstrate the predictive capability of our technique through a careful set of numerical results.

Summary

We haven't generated a summary for this paper yet.