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 3. Problem setting Illustration of the proposed IIL setting is shown in Fig. 1. As can be seen, data is generated continually and unpredictably in the data stream. Generally in real application, people incline to collect enough data first and train a strong model M0 for deployment. No matter how strong the model is, it inevitably will encounter out-of-distribution data and fail on it. These failed cases and other low-score new observations will be annotated to train the model from time to time. Retraining the model with all cumulate data every time leads to higher and higher cost in time and resource. Therefore, the new IIL aims to enhance the existing model with only the new data each time. 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

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New IIL Setting: Enhancing Deployed Models with Only New Data

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