Fine-grained Angular Contrastive Learning with Coarse Labels

12/07/2020
by   Guy Bukchin, et al.
0

Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This adaptivity to unseen classes is especially important for many practical applications where the pre-trained label space cannot remain fixed for effective use and the model needs to be "specialized" to support new categories on the fly. One particularly interesting scenario, essentially overlooked by the few-shot literature, is Coarse-to-Fine Few-Shot (C2FS), where the training classes (e.g. animals) are of much `coarser granularity' than the target (test) classes (e.g. breeds). A very practical example of C2FS is when the target classes are sub-classes of the training classes. Intuitively, it is especially challenging as (both regular and few-shot) supervised pre-training tends to learn to ignore intra-class variability which is essential for separating sub-classes. In this paper, we introduce a novel 'Angular normalization' module that allows to effectively combine supervised and self-supervised contrastive pre-training to approach the proposed C2FS task, demonstrating significant gains in a broad study over multiple baselines and datasets. We hope that this work will help to pave the way for future research on this new, challenging, and very practical topic of C2FS classification.

READ FULL TEXT

page 1

page 4

page 8

page 10

research
10/14/2022

Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning

Novel category discovery aims at adapting models trained on known catego...
research
03/01/2020

Novelty-Prepared Few-Shot Classification

Few-shot classification algorithms can alleviate the data scarceness iss...
research
09/13/2021

Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning

In this work, we focus on a more challenging few-shot intent detection s...
research
03/14/2020

TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification

The field of Few-Shot Learning (FSL), or learning from very few (typical...
research
06/08/2021

Coarse-to-Fine Curriculum Learning

When faced with learning challenging new tasks, humans often follow sequ...
research
06/28/2020

Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy

We study many-class few-shot (MCFS) problem in both supervised learning ...
research
05/09/2023

Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training

New roads are being constructed all the time. However, the capabilities ...

Please sign up or login with your details

Forgot password? Click here to reset