New IIL Setting: Enhancing Deployed Models with Only New Data

Table of Links

Abstract and 1 Introduction

2. Related works

3. Problem setting

4. Methodology

   4.1. Decision boundary-aware distillation

   4.2. Knowledge consolidation

5. Experimental results and 5.1. Experiment Setup

   5.2. Comparison with SOTA methods

   5.3. Ablation study

6. Conclusion and future work and References

Supplementary Material

7. Details of the theoretical analysis on KCEMA mechanism in IIL
8. Algorithm overview
9. Dataset details
10. Implementation details
11. Visualization of dusted input images
12. 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.