Neighborhood contrastive learning for novel class discovery
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in
a set of unlabeled samples given a labeled dataset with known classes. We exploit the …
a set of unlabeled samples given a labeled dataset with known classes. We exploit the …
Modeling inter-class and intra-class constraints in novel class discovery
Novel class discovery (NCD) aims at learning a model that transfers the common knowledge
from a class-disjoint labelled dataset to another unlabelled dataset and discovers new …
from a class-disjoint labelled dataset to another unlabelled dataset and discovers new …
Bootstrap your own prior: Towards distribution-agnostic novel class discovery
Abstract Novel Class Discovery (NCD) aims to discover unknown classes without any
annotation, by exploiting the transferable knowledge already learned from a base set of …
annotation, by exploiting the transferable knowledge already learned from a base set of …
Divide and conquer: Compositional experts for generalized novel class discovery
In response to the explosively-increasing requirement of annotated data, Novel Class
Discovery (NCD) has emerged as a promising alternative to automatically recognize …
Discovery (NCD) has emerged as a promising alternative to automatically recognize …
A unified objective for novel class discovery
In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring
novel object categories in an unlabeled set by leveraging from prior knowledge of a labeled …
novel object categories in an unlabeled set by leveraging from prior knowledge of a labeled …
Nearest neighbor matching for deep clustering
Deep clustering gradually becomes an important branch in unsupervised learning methods.
However, current approaches hardly take into consideration the semantic sample …
However, current approaches hardly take into consideration the semantic sample …
Openmix: Reviving known knowledge for discovering novel visual categories in an open world
In this paper, we tackle the problem of discovering new classes in unlabeled visual data
given labeled data from disjoint classes. Existing methods typically first pre-train a model …
given labeled data from disjoint classes. Existing methods typically first pre-train a model …
Contrastive clustering
In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …
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 unlabeled set by
leveraging prior knowledge from a labeled set with known classes. Despite its importance …
leveraging prior knowledge from a labeled set with known classes. Despite its importance …
Effective sample pairs based contrastive learning for clustering
As an indispensable branch of unsupervised learning, deep clustering is rapidly emerging
along with the growth of deep neural networks. Recently, contrastive learning paradigm has …
along with the growth of deep neural networks. Recently, contrastive learning paradigm has …