SlowGuess/NeurIPS_2023_OCR huggingface dataset:
For the hyperparameters setting, we set  $\alpha = 0.5$ ,  $\tau = 16$  for miniImageNet and CUB200,  $\alpha = 0.1$ ,  $\tau = 16$  for CIFAR100. We train the feature extractor on CUB200 with a learning rate of 0.004,
batch size of 128, and epochs of 400. We train the feature extractor on CIFAR100 and miniImageNet with a learning rate of 0.1 and batch size of 256. The cosine scheduler is used to adjust the learning rate. Before computing the cross-entropy loss, a widely-used temperature scalar is used to adjust the distribution of output logits. For example, the original output logits of instance  $x_{j}$  are denoted as  $\mathcal{O}_j\in \mathbb{R}^C$ . The logits used to compute cross-entropy loss are denoted as  $\mathcal{O}_j\tau_o$ . The  $\tau_{o}$  is set to 16 for the CIFAR100 dataset and 32 for miniImageNet and CUB200 datasets.
C.2 More comparison results
In the main paper, we compare different methods in overall performance measures: performance drop rate, average accuracy across all classes, harmonic mean accuracy and average accuracy on new classes. In this session, we report the detailed comparison results of the aforementioned performance measures on CIFAR100 [23] and CUB200 [44]. Besides the main results in Table 6 and Table 7, we also report the accuracy across new classes and harmonic mean accuracy in Table 8. The consistent performance improvement demonstrates the effectiveness of TEEN's calibration ability.
D Empirical Analysis
D.1 Effects of TEEN
In the main paper, we only show the effect of calibration prototypes on the CIFAR100 dataset. In this supplementary material, we report more detailed results in Table 9. The results further confirm our conclusion in the main paper: the proposed TEEN can well calibrate the biased prototype of new classes. Although TEEN leads to misclassifying some instances of base classes, the average accuracy across all classes is always very competitive.
Table 9: The detailed ratio (\%) of base and new classes about three types of samples(i.e., UC samples, W→R samples, R→W samples) of miniImageNet
Incremental Session	1	2	3	4
Sample type	UC	W→R	R→W	UC	W→R	R→W	UC	W→R	R→W	UC	W→R	R→W
Base Class	93.84	0.00	99.99	88.87	0.00	95.78	84.15	0.00	94.20	79.65	0.00	91.03
New Class	6.16	100.00	0.01	11.13	100.00	4.22	15.85	100.00	5.80	20.35	100.00	8.97
D.2 Combination of TEEN with existing methods
Due to the flexibility of proposed TEEN, it can be seen as a plug-and-play module to calibrate the biased prototypes of new classes. For a more in-depth understanding of the TEEN, we combine TEEN with FACT [58] and show the performance on different performance measures in Figure 6, Figure 7 and Figure 8. We empirically find that combining the TEEN with FACT has little effect on the average accuracy across all classes (i.e., the left figures in Figure 6, Figure 7 and Figure 8). However, the TEEN can improve the average accuracy of new classes and the harmonic mean accuracy. In other words, the existing prototype-based methods (i.e., FACT) benefit the well-calibration effect of TEEN and achieve better-balanced performance between the base and new classes. We believe more methods (e.g., [52]) can benefit from the flexibility of TEEN.
E Broader impact
Our proposed method can significantly improve the accuracy of new classes in FSCIL and FSL scenarios, leading to a better understanding of the low performance of new classes. Furthermore, the analyses in this study are first proposed in the FSCIL and FSL fields, which point out that we should pay more attention to the performance of new classes. In addition to this, our method may also have potential heuristic effects on the expectation estimation of the distribution.
(a) Accuracy on all classes
(b) Harmonic mean accuracy
(c) Accuracy on new classes
Figure 6: We compare FACT without (i.e., FACT in figures) and with TEEN (i.e., FACT w/ TEEN on CUB200 dataset. The FACT benefits from the well-calibration effect of TEEN and achieves better-balanced performance between the base and new classes
(a) Accuracy on all classes
(a) Accuracy on all classes
Figure 8: We compare FACT without (i.e., FACT in figures) and with TEEN (i.e., FACT w/ TEEN on CIFAR100 dataset. The FACT benefits from the well-calibration effect of TEEN and achieves better-balanced performance between the base and new classes
Figure 7: We compare FACT without (i.e., FACT in figures) and with TEEN (i.e., FACT w/ TEEN on miniImageNet dataset. The FACT benefits from the well-calibration effect of TEEN and achieves better-balanced performance between the base and new classes
(c) Accuracy on new classes
Table 10: Detailed prediction results of False Negative Rate/False Positive Rate (\%) on miniImageNet dataset. The analysis results are from session 1 because new classes do not exist in session 0. Exceedingly high FPR and relatively low FNR show the instances of new classes are easily misclassified into base classes and the instances of base classes are also easily misclassified into base classes. TEEN can achieve relatively lower FPR than baseline methods, which demonstrates the validity of the proposed calibration strategy.
Session	1	2	3	4	5	6	7	8
FNR/FPR	FNR FPR	FNR FPR	FNR FPR	FNR FPR	FNR FPR	FNR FPR	FNR FPR	FNR FPR
ProtoNet [40]	2.55 71.60	3.98 67.30	4.83 66.07	5.83 62.05	6.48 59.16	7.18 58.17	7.93 54.83	8.85 52.23
CEC [57]	3.45 68.40	5.60 65.50	7.03 61.93	7.73 58.30	8.47 56.68	9.58 54.50	10.22 52.54	10.93 50.05
FACT [58]	2.07 72.40	3.42 70.60	4.22 70.13	4.55 69.40	4.97 68.28	5.12 68.17	5.33 66.51	5.58 66.55
TEEN	8.02 46.40	11.35 38.60	13.12 37.53	15.32 35.20	16.47 32.48	17.38 31.57	18.63 28.03	19.97 26.35
Table 11: Detailed prediction results of TNR/TBR (\%) on miniImageNet dataset. The analysis results are from session 1 because new classes do not exist in session 0. For new classes, we only consider the 10 most similar base classes out of 60 base classes. For base classes, we suppose  $C_i$  new classes exist in the current incremental session  $i$ . We only consider the most similar  $\lfloor 20\% \times C_i \rfloor$  new classes. Class similarity adopts cosine similarity between different class prototypes. TEEN can achieve relatively lower TBR than baseline methods, which demonstrates the validity of the proposed calibration strategy.
Session	1	2	3	4	5	6	7	8
TNR/TBR	TNR TBR	TNR TBR	TNR TBR	TNR TBR	TNR TBR	TNR TBR	TNR TBR	TNR TBR
ProtoNet [40]	3.47	73.01	5.02	61.79	7.06	55.05	8.08	52.95
CEC [57]	2.87	71.70	4.53	61.08	5.66	58.26	6.35	55.16
FACT [58]	2.33	70.00	3.32	61.01	5.50	55.17	6.88	50.55
TEEN	2.16	66.77	2.53	55.14	3.55	44.87	3.85	37.68
3.57	39.47
4.02	37.07
4.76	33.83
8.20	51.64
8.24	49.96
8.88	46.99
9.31	48.48