GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning

2021-02-15 · Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya

Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially compelling due to the strong representational power induced by the network. However, inference in GPs, whether with or without DKL, can be computationally challenging on large datasets. Here, we propose GP-Tree, a novel method for multi-class classification with Gaussian processes and DKL. We develop a tree-based hierarchical model in which each internal node of the tree fits a GP to the data using the P\'olya Gamma augmentation scheme. As a result, our method scales well with both the number of classes and data size. We demonstrate the effectiveness of our method against other Gaussian process training baselines, and we show how our general GP approach achieves improved accuracy on standard incremental few-shot learning benchmarks.

📄 PDF Abstract BibTeX arXiv:2102.07868

Code (1)

IdanAchituve/GP-Tree 공식 구현 pytorch

Tasks

Few-Shot Learning Gaussian Processes Incremental Learning Multi-class Classification

Methods 이 논문이 사용한 방법론

DKL 설명 없음

GPS Greedy Policy Search (GPS) is a simple algorithm that learns a policy for test-time data augmentation based on the predictive performance on a validation set. GPS starts with…

Gaussian Process Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for…

Polya-Gamma Augmentation This method applies Polya-Gamma latent variables as a way to obtain closed form expressions for full-conditionals of posterior distributions in sampling algorithms like MCMC.

SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation

2024-12-15 · Xudong Zhou, Wenhao He

We propose SAM-IF, a novel method for incremental few-shot instance segmentation leveraging the Segment Anything Model (SAM). SAM-IF addresses the challenges of class-agnostic instance segmentation by introducing a multi…

Decoder Few-shot Instance Segmentation Few-Shot Learning Incremental Learning+3

Personalizing human activity recognition models using incremental learning

2019-05-29 · Pekka Siirtola, Heli Koskimäki, Juha Röning

In this study, the aim is to personalize inertial sensor data-based human activity recognition models using incremental learning. At first, the recognition is based on user-independent model. However, when personal strea…

Activity Recognition General Classification Human Activity Recognition Incremental Learning

Continual Learning in Open-vocabulary Classification with Complementary Memory Systems

2023-07-04 · Zhen Zhu, Weijie Lyu, Yao Xiao, Derek Hoiem

We introduce a method for flexible and efficient continual learning in open-vocabulary image classification, drawing inspiration from the complementary learning systems observed in human cognition. Specifically, we propo…

Continual Learning image-classification Image Classification Open Vocabulary Image Classification

Few-shot Class-Incremental Learning via Generative Co-Memory Regularization

2026-01-12 · Kexin Bao, Yong Li, Dan Zeng, Shiming Ge arxiv

Few-shot class-incremental learning (FSCIL) aims to incrementally learn models from a small amount of novel data, which requires strong representation and adaptation ability of models learned under few-example supervisio…

Few-Shot Class-Incremental Learning Domain Adaptation

MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning

2023-04-08 · Anant Khandelwal

Few Shot Class Incremental Learning (FSCIL) with few examples per class for each incremental session is the realistic setting of continual learning since obtaining large number of annotated samples is not feasible and co…

class-incremental learning Class Incremental Learning Continual Learning Few-Shot Class-Incremental Learning+1