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Poster
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Massimiliano Patacchiola · John Bronskill · Aliaksandra Shysheya · Katja Hofmann · Sebastian Nowozin · Richard Turner

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #237
in Poster Session 3 »

Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a pretrained system is adapted by learning on small amounts of labeled data belonging to a specific user. This setting requires high accuracy under low computational complexity, therefore the Pareto frontier of accuracy vs. adaptation cost plays a crucial role. In this paper we push this Pareto frontier in the few-shot image classification setting with a key contribution: a new adaptive block called Contextual Squeeze-and-Excitation (CaSE) that adjusts a pretrained neural network on a new task to significantly improve performance with a single forward pass of the user data (context). We use meta-trained CaSE blocks to conditionally adapt the body of a network and a fine-tuning routine to adapt a linear head, defining a method called UpperCaSE. UpperCaSE achieves a new state-of-the-art accuracy relative to meta-learners on the 26 datasets of VTAB+MD and on a challenging real-world personalization benchmark (ORBIT), narrowing the gap with leading fine-tuning methods with the benefit of orders of magnitude lower adaptation cost.

Keywords: Meta-Learning · Few-Shot Learning · transfer learning
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Massimiliano Patacchiola (University of Cambridge)
Massimiliano Patacchiola

Massimiliano (Max) Patacchiola is a postdoctoral researcher at the University of Cambridge (Machine Learning Group) working under the supervision of prof. Richard Turner in collaboration with Microsoft Research. Before he was a postdoctoral researcher at the University of Edinburgh and an inter at Snapchat. Max is interested in meta-learning, few-shot learning, and reinforcement learning.

John Bronskill (University of Cambridge)
Aliaksandra Shysheya (University of Cambridge)
Katja Hofmann (Microsoft Research)

Dr. Katja Hofmann is a Principal Researcher at the [Game Intelligence](http://aka.ms/gameintelligence/) group at [Microsoft Research Cambridge, UK](https://www.microsoft.com/en-us/research/lab/microsoft-research-cambridge/). There, she leads a research team that focuses on reinforcement learning with applications in modern video games. She and her team strongly believe that modern video games will drive a transformation of how we interact with AI technology. One of the projects developed by her team is [Project Malmo](https://www.microsoft.com/en-us/research/project/project-malmo/), which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Katja's long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems. Before joining Microsoft Research, Katja completed her PhD in Computer Science as part of the [ILPS](https://ilps.science.uva.nl/) group at the [University of Amsterdam](https://www.uva.nl/en). She worked with Maarten de Rijke and Shimon Whiteson on interactive machine learning algorithms for search engines.

Sebastian Nowozin (DeepMind)
Richard Turner (University of Cambridge)

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