Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning (2022)

Abstract: Novel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of the same granularity.In this paper, we investigate a new practical scenario called Fine-grained Category Discovery under Coarse-grained supervision (FCDC). FCDC aims at discovering fine-grained categories with only coarse-grained labeled data, which can adapt models to categories of different granularity from known ones and reduce significant labeling cost. It is also a challenging task since supervised training on coarse-grained categories tends to focus on inter-class distance (distance between coarse-grained classes) but ignore intra-class distance (distance between fine-grained sub-classes) which is essential for separating fine-grained categories.Considering most current methods cannot transfer knowledge from coarse-grained level to fine-grained level, we propose a hierarchical weighted self-contrastive network by building a novel weighted self-contrastive module and combining it with supervised learning in a hierarchical manner.Extensive experiments on public datasets show both effectiveness and efficiency of our model over compared methods.

## introduction discovering novel categories based on some known categories has attracted much attention in both natural language processing @xcite and computer vision @xcite . previous works assume that novel categories are of the same granularity (or of the same class hierarchy level) as known categories. however, in real-world scenarios, novel categories can be more fine-grained sub-categories of known ones (e.g., sports and tennis). a typical application of this scenario is when data analysts want to perform more fine-grained analysis on data with only coarse-grained annotations, where re-labeling fine-grained categories can be time consuming and labour intensive. for example, in the intent detection field, discovering more fine-grained user intents can help to provide better services to customers, but labeling fine-grained intent categories is often much more difficult than labeling coarsegrained ones, since fine-grained annotation often requires higher expertise. to meet this requirement, we investigate a new scenario named fine-grained category discovery under coarse-grained supervision (fcdc). as shown in figure 1 , fcdc needs models to discover fine-grained categories (e.g., tennis and music) based only on coarse-grained (e.g., sports and arts) labeled data which are easier and cheaper to obtain. in addition to being in line with above practical needs, fcdc is also a challenging task. firstly, performing fcdc requires models to increase intra-class distance to ensure fine-grained separability with only coarse-grained supervision. however, coarse-grained classification only focuses on inter-class distance and does not care about intraclass distance @xcite , so samples with the same coarse-grained labels will be close to each other and hard to be separated in finegrained feature space. secondly, since fine-grained differentiation depends on correct coarse-grained classification, fcdc also requires models to con-trol inter-class distance to ensure coarse-grained separability. although increasing intra-class distance can contribute to fine sub-classes separability, it will also decrease inter-class distance, which can result in overlapping between different coarsegrained classes and therefore lead to misclassification. so how to control and coordinate interclass and intra-class distance to ensure both coarse-grained and fine-grained separability is the core challenge of fcdc. to address above challenges and transfer knowledge from coarse-grained level to fine-grained level, we propose a hierarchical weighted selfcontrastive network. by performing different experiments on each layer of bert, @xcite find bottom layers of bert capture more surface features and top layers capture more highlevel semantic features, which means bert can extract features of different granularities from shallow to deep @xcite . inspired by this phenomenon, the core motivation of our model is to learn coarse-grained knowledge by shallow layers of bert and learn more fine-grained knowledge by the rest of deep layers hierarchically. this motivation is not only consistent with the feature extraction process of bert, but also corresponding with the shallow-to-deep learning process of humans. specifically, we use given coarse-grained labels to train shallow layers of bert to learn some surface knowledge, then we propose a weighted selfcontrastive module to train deep layers of bert to learn more fine-grained knowledge based on the learned surface knowledge. to ensure both coarse-grained and fine-grained separability, we further propose a weighted selfcontrastive module to better coordinate inter-class and intra-class distance in the fine-grained feature space. specifically, given a query sample, we firstly propose a weighting strategy by weighting different negative samples to control both inter-class and intra-class distance. then we propose a selfcontrastive strategy to generate positive samples to coordinate inter-class and intra-class distance to avoid the overlapping between different coarsegrained classes. we further verify effectiveness and efficiency of our model both theoretically (section 3.2.4) and experimentally (section 4.5). the main contributions of our work can be summarized as threefold: ## proposed approach as shown in figure 2 , our model mainly contains three components: bert, dynamic queue and momentum bert. bert is used to extract both coarse-grained and fine-grained features. dynamic queue can store more negative samples grouping by their coarse-grained labels following @xcite . momentum bert is used to update representations of samples in dynamic queue. inspired by the "shallow to deep" learning process of humankind and the ability of pre-trained models to extract features from coarse-grained to fine-grained @xcite , a core motivation of our model is to learn fine-grained knowledge in a progressive way. specifically, our model can learn coarse-grained knowledge at shallow layers under coarse-grained supervision and learn more fine-grained knowledge at deep layers with the proposed weighted self-contrastive learning.