Continual Learning on mini-Imagenet

Metric: Average Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Average Accuracy▼	Augmentations	Paper	Date↕	Code
1	PriViLege	95.27	No	Pre-trained Vision and Language Transformers Are...	2024-04-02	Code
2	SV-T	85.07	No	Semantic-visual Guided Transformer for Few-shot ...	2023-03-27	-
3	NC-FSCIL	67.82	No	-	-	Code
4	C-FSCIL	61.61	No	Constrained Few-shot Class-incremental Learning	2022-03-30	Code
5	FACT	60.7	No	Forward Compatible Few-Shot Class-Incremental Le...	2022-03-14	Code
6	LIMIT	59.06	No	Few-Shot Class-Incremental Learning by Sampling ...	2022-03-31	Code
7	CEC	57.74	No	Few-Shot Incremental Learning with Continually E...	2021-04-07	Code
8	F2M	54.89	No	Overcoming Catastrophic Forgetting in Incrementa...	2021-10-30	Code
9	IDLVQ-C	51.16	No	-	-	-
10	AL-MML	39.64	No	Few-Shot Class-Incremental Learning	2020-04-23	Code

#1PriViLegeSOTA
95.27
Average Accuracy· 2024-04-02
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners Code
#2SV-TSOTA
85.07
Average Accuracy· 2023-03-27
Semantic-visual Guided Transformer for Few-shot Class-incremental Learning
#3NC-FSCIL
67.82
Average Accuracy
No paperCode
#4C-FSCILSOTA
61.61
Average Accuracy· 2022-03-30
Constrained Few-shot Class-incremental Learning Code
#5FACTSOTA
60.7
Average Accuracy· 2022-03-14
Forward Compatible Few-Shot Class-Incremental Learning Code
#6LIMIT
59.06
Average Accuracy· 2022-03-31
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks Code
#7CECSOTA
57.74
Average Accuracy· 2021-04-07
Few-Shot Incremental Learning with Continually Evolved Classifiers Code
#8F2M
54.89
Average Accuracy· 2021-10-30
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima Code
#9IDLVQ-C
51.16
Average Accuracy
No paper
#10AL-MMLSOTA
39.64
Average Accuracy· 2020-04-23
Few-Shot Class-Incremental Learning Code