Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Studious Approach to Semi-Supervised Learning (2109.08924v1)

Published 18 Sep 2021 in cs.CV

Abstract: The problem of learning from few labeled examples while using large amounts of unlabeled data has been approached by various semi-supervised methods. Although these methods can achieve superior performance, the models are often not deployable due to the large number of parameters. This paper is an ablation study of distillation in a semi-supervised setting, which not just reduces the number of parameters of the model but can achieve this while improving the performance over the baseline supervised model and making it better at generalizing. After the supervised pretraining, the network is used as a teacher model, and a student network is trained over the soft labels that the teacher model generates over the entire unlabeled data. We find that the fewer the labels, the more this approach benefits from a smaller student network. This brings forward the potential of distillation as an effective solution to enhance performance in semi-supervised computer vision tasks while maintaining deployability.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Sahil Khose (9 papers)
  2. Shruti Jain (5 papers)
  3. V Manushree (4 papers)

Summary

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