RNNP: A Robust Few-Shot Learning Approach

11/22/2020
by   Pratik Mazumder, et al.
11

Learning from a few examples is an important practical aspect of training classifiers. Various works have examined this aspect quite well. However, all existing approaches assume that the few examples provided are always correctly labeled. This is a strong assumption, especially if one considers the current techniques for labeling using crowd-based labeling services. We address this issue by proposing a novel robust few-shot learning approach. Our method relies on generating robust prototypes from a set of few examples. Specifically, our method refines the class prototypes by producing hybrid features from the support examples of each class. The refined prototypes help to classify the query images better. Our method can replace the evaluation phase of any few-shot learning method that uses a nearest neighbor prototype-based evaluation procedure to make them robust. We evaluate our method on standard mini-ImageNet and tiered-ImageNet datasets. We perform experiments with various label corruption rates in the support examples of the few-shot classes. We obtain significant improvement over widely used few-shot learning methods that suffer significant performance degeneration in the presence of label noise. We finally provide extensive ablation experiments to validate our method.

READ FULL TEXT
research
12/16/2021

Semantic-Based Few-Shot Learning by Interactive Psychometric Testing

Few-shot classification tasks aim to classify images in query sets based...
research
05/29/2021

Multi-Label Few-Shot Learning for Aspect Category Detection

Aspect category detection (ACD) in sentiment analysis aims to identify t...
research
06/07/2020

Distributionally Robust k-Nearest Neighbors for Few-Shot Learning

Learning a robust classifier from a few samples remains a key challenge ...
research
10/21/2022

AROS: Affordance Recognition with One-Shot Human Stances

We present AROS, a one-shot learning approach that uses an explicit repr...
research
06/17/2020

Improving Few-Shot Visual Classification with Unlabelled Examples

We propose a transductive meta-learning method that uses unlabelled inst...
research
10/31/2022

Learning New Tasks from a Few Examples with Soft-Label Prototypes

It has been experimentally demonstrated that humans are able to learn in...
research
06/02/2021

One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations

The field of few-shot learning has made remarkable strides in developing...

Please sign up or login with your details

Forgot password? Click here to reset