Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Related works


Problem setting


Methodology
4.1. Decision boundary-aware distillation
4.2. Knowledge consolidation


Experimental results and 5.1. Experiment Setup
5.2. Comparison with SOTA methods
5.3. Ablation study


Conclusion and future work and References Related works Related works Related works Problem setting Problem setting Problem setting Methodology
4.1. Decision boundary-aware distillation
4.2. Knowledge consolidation Methodology Methodology 4.1. Decision boundary-aware distillation 4.1. Decision boundary-aware distillation 4.2. Knowledge consolidation 4.2. Knowledge consolidation Experimental results and 5.1. Experiment Setup
5.2. Comparison with SOTA methods
5.3. Ablation study Experimental results and 5.1. Experiment Setup Experimental results and 5.1. Experiment Setup 5.2. Comparison with SOTA methods 5.2. Comparison with SOTA methods 5.3. Ablation study 5.3. Ablation study Conclusion and future work and References Conclusion and future work and References Conclusion and future work and References Supplementary Material Supplementary Material Details of the theoretical analysis on KCEMA mechanism in IIL
Algorithm overview
Dataset details
Implementation details
Visualization of dusted input images
More experimental results Details of the theoretical analysis on KCEMA mechanism in IIL Details of the theoretical analysis on KCEMA mechanism in IIL Algorithm overview Algorithm overview Dataset details Dataset details Implementation details Implementation details Visualization of dusted input images Visualization of dusted input images More experimental results More experimental results 8. Algorithm overview The whole process of the proposed method is illustrated in Algorithm 1. Codes will be released to public upon publication. 9. Dataset details Corresponding to the definition of our new increment-alinstance learning (IIL) task, we reorganize the public dataset Cifar-100 [11] and ImageNet-100 [24] to establish the benchmark in this research topic. As can be seen, the base dataset has a more balance sample distribution, where the largest class is class 6 with 279 images. The smallest class is class 29 and has 215 images. Incremental dataset “D5” has the most imbalance number of samples between classes, where the maximum number is 40 in class 58 and the minimum number is 12 in class 76. That is, the highest imbalance ratio is 3.33 : 1. Authors:
(1) Qiang Nie, Hong Kong University of Science and Technology (Guangzhou);
(2) Weifu Fu, Tencent Youtu Lab;
(3) Yuhuan Lin, Tencent Youtu Lab;
(4) Jialin Li, Tencent Youtu Lab;
(5) Yifeng Zhou, Tencent Youtu Lab;
(6) Yong Liu, Tencent Youtu Lab;
(7) Qiang Nie, Hong Kong University of Science and Technology (Guangzhou);
(8) Chengjie Wang, Tencent Youtu Lab. Authors: Authors: (1) Qiang Nie, Hong Kong University of Science and Technology (Guangzhou); (2) Weifu Fu, Tencent Youtu Lab; (3) Yuhuan Lin, Tencent Youtu Lab; (4) Jialin Li, Tencent Youtu Lab; (5) Yifeng Zhou, Tencent Youtu Lab; (6) Yong Liu, Tencent Youtu Lab; (7) Qiang Nie, Hong Kong University of Science and Technology (Guangzhou); (8) Chengjie Wang, Tencent Youtu Lab. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. This paper is available on arxiv under CC BY-NC-ND 4.0 Deed (Attribution-Noncommercial-Noderivs 4.0 International) license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Algorithm and Data Imbalance: Implementing the New IIL Benchmarks

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

A Coarse-to-Fine Approach to Text-Guided 3D Localization Without Ground Truth

Few-Shot Personalization of YOLOv8 with Object-Conditioned Bags of Instances

Add New Items, Not New Weights - Personalize Detection Without Backprop

Data Scarcity Solution: S-CycleGAN for CT-to-Ultrasound Translation

New IIL Setting: Enhancing Deployed Models with Only New Data

A Coarse-to-Fine Approach to Text-Guided 3D Localization Without Ground Truth

Few-Shot Personalization of YOLOv8 with Object-Conditioned Bags of Instances

Add New Items, Not New Weights - Personalize Detection Without Backprop

Data Scarcity Solution: S-CycleGAN for CT-to-Ultrasound Translation

New IIL Setting: Enhancing Deployed Models with Only New Data

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps