A Closer Look at Novel Class Discovery from the Labeled Set

by   Ziyun Li, et al.

Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes. Existing research focuses primarily on utilizing the labeled set at the methodological level, with less emphasis on the analysis of the labeled set itself. Thus, in this paper, we rethink novel class discovery from the labeled set and focus on two core questions: (i) Given a specific unlabeled set, what kind of labeled set can best support novel class discovery? (ii) A fundamental premise of NCD is that the labeled set must be related to the unlabeled set, but how can we measure this relation? For (i), we propose and substantiate the hypothesis that NCD could benefit more from a labeled set with a large degree of semantic similarity to the unlabeled set. Specifically, we establish an extensive and large-scale benchmark with varying degrees of semantic similarity between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical class structure. As a sharp contrast, the existing NCD benchmarks are developed based on labeled sets with different number of categories and images, and completely ignore the semantic relation. For (ii), we introduce a mathematical definition for quantifying the semantic similarity between labeled and unlabeled sets. In addition, we use this metric to confirm the validity of our proposed benchmark and demonstrate that it highly correlates with NCD performance. Furthermore, without quantitative analysis, previous works commonly believe that label information is always beneficial. However, counterintuitively, our experimental results show that using labels may lead to sub-optimal outcomes in low-similarity settings.


Supervised Knowledge May Hurt Novel Class Discovery Performance

Novel class discovery (NCD) aims to infer novel categories in an unlabel...

Novel Class Discovery in Semantic Segmentation

We introduce a new setting of Novel Class Discovery in Semantic Segmenta...

A Unified Objective for Novel Class Discovery

In this paper, we study the problem of Novel Class Discovery (NCD). NCD ...

Mutual Information-guided Knowledge Transfer for Novel Class Discovery

We tackle the novel class discovery problem, aiming to discover novel cl...

Generalized Category Discovery with Decoupled Prototypical Network

Generalized Category Discovery (GCD) aims to recognize both known and no...

Novel Class Discovery: an Introduction and Key Concepts

Novel Class Discovery (NCD) is a growing field where we are given during...

When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis

Novel Class Discovery (NCD) aims at inferring novel classes in an unlabe...

Please sign up or login with your details

Forgot password? Click here to reset