category discovery
Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery
CPG enables the model to fully capture the intra-category diversity by representing each category with multiple prototypes.
Learn to Categorize or Categorize to Learn Self Coding for Generalized Category Discovery Supplementary Materials
ImageNet-100 is a subset of 100 categories from the coarse-grained ImageNet [13] dataset.
A Proof for Claim
CIFAR-10-L T, CIFAR-100-L T, ImageNet-100-L T, and Places-L T are 5, 80, 50, and 182 respectively. Our default training set of each dataset is summarized in Table 8.
Towards Distribution-Agnostic Generalized Category Discovery
While several previous works have focused on classifying close-set samples and detecting open-set samples during testing, it's still essential to be able to classify unknown subjects as human beings.
Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery
Recent advancements have shown promise in applying traditional Semi-Supervised Learning strategies to the task of Generalized Category Discovery (GCD).
ContinuousCategoriesDiscovery
We refer to it as theContinuous Category Discovery(CCD) problem, which is significantly more challenging than the static setting.
No Representation Rules Them All in Category Discovery
In this paper we tackle the problem of Generalized Category Discovery (GCD).