Name		Name	Last commit message	Last commit date
parent directory ..
BOIL		BOIL
Baseline++		Baseline++
Baseline		Baseline
CAN		CAN
CovaM		CovaM
Proto		Proto
R2D2		R2D2
RENet		RENet
RFS		RFS
RelationNet		RelationNet
SKD		SKD
README.md		README.md
TEMPLATE.md		TEMPLATE.md

README.md

Reproducible configs and checkpoints

This folder contains:

Reproducible config of Table.1 in the paper of LibFewShot.
Reproducible config of Table.2 in the paper of LibFewShot.

Reproduction results on miniImageNet

(Results may different from the paper. Here are up-to-date results with checkpoints and configs.)

Method	Embed.	5-way 1-shot		5-way 5-shot
Method	Embed.	Reported	Ours	Reported	Ours
Baseline	Conv64F	42.11	-	62.53	-
Baseline	ResNet18	51.75	51.18 ± 0.34	74.27	74.06 ± 0.28
Baseline++	Conv64F	48.24	-	66.43	-
Baseline++	ResNet18	51.87	-	75.68	-
RFS-simple	ResNet12	62.02 ± 0.63	62.80 ± 0.52	79.64 ± 0.44	79.57± 0.39
RFS-distill	ResNet12	64.82	63.44	82.14	80.17
SKD-GEN0	ResNet12	65.93	66.40	83.15	83.06
SKD-GEN1	ResNet12	67.04	67.35	83.54	80.30
RENet	ResNet12	67.60 ± 0.44	66.83 ± 0.36	82.58 ± 0.30	82.13 ± 0.26
MAML	Conv32F	48.70	47.41	63.11	65.24
Versa	Conv64F†	53.40	51.92	67.37	66.26
R2D2	Conv64F	49.50	47.57	65.40	66.68
R2D2	Conv64F‡	51.80	55.53	68.40	70.79
ANIL	Conv32F	46.70	48.44	61.50	64.35
BOIL	Conv64F	49.61 ± 0.16	**48.00 ± 0.36	66.45 ± 0.37	**64.39 ± 0.30
BOIL	ResNet12**	-	**58.87 ± 0.38	71.30 ± 0.28	**72.88 ± 0.29
MTL	ResNet12	60.20	60.20	74.30	75.86
ProtoNet†	Conv64F	46.14	46.30	65.77	66.24
RelationNet	Conv64F	50.44	51.75	65.32	66.77
CovaMNet	Conv64F	51.19	53.36	67.65	68.17
DN4	Conv64F	51.24	51.95	71.02	71.42
DN4	ResNet12†	54.37	57.76	74.44	77.57
CAN	ResNet12	63.85	66.62	79.44	78.96
NegCos	ResNet12	63.85 ± 0.81	63.28 ± 0.36	81.57 ± 0.56	81.24 ± 0.26

The overview picture of the SOTAs

Conv64F

Method	Venue	Type	miniImageNet		tieredImageNet
Method	Venue	Type	1-shot	5-shot	1-shot	5-shot
Baseline	ICLR’19	Fine-tuning	44.90 ± 0.32	63.96 ± 0.30	48.20 ± 0.35	68.96 ± 0.33
Baseline++	ICML’19	Fine-tuning	48.86 ± 0.35	63.29 ± 0.30	55.94 ± 0.39	73.80 ± 0.32
RFS-simple	ECCV’20	Fine-tuning	47.97 ± 0.33	65.88 ± 0.30	52.21 ± 0.37	71.82 ± 0.32
RFS-distill	ECCV’20	Fine-tuning	-	-	-	-
SKD-GEN0	arXiv’20	Fine-tuning	48.14 ± 0.33	66.36 ± 0.29	51.78 ± 0.36	70.65 ± 0.32
SKD-GEN1	arXiv’20	Fine-tuning	-	-	-	-
NegCos	ECCV’20	Fine-tuning	47.34	65.97	51.21	71.57
RENet	ICCV’21	Fine-tuning	57.62 ± 0.36	74.14 ± 0.27	61.62 ± 0.40	76.74 ± 0.33
MAML	ICML’17	Meta	49.55 ± 0.37	64.92 ± 0.32	50.98 ± 0.43	67.12 ± 0.35
Versa	NeurIPS’18	Meta	52.75 ± 0.38	67.40 ± 0.31	52.28 ± 0.99	69.41 ± 0.37
R2D2	ICLR’19	Meta	51.19 ± 0.36	67.29 ± 0.31	52.18 ± 0.40	69.19 ± 0.36
LEO	ICLR’19	Meta	53.31 ± 0.37	67.47 ± 0.30	58.15 ± 0.40	74.21 ± 0.33
MTL	CVPR’19	Meta	40.97	57.12	42.36	64.87
ANIL	ICLR’20	Meta	48.01 ± 0.35	63.88 ± 0.32	49.05 ± 0.39	66.32 ± 0.34
BOIL	ICLR’21	Meta	47.92 ± 0.35	64.39 ± 0.30	50.04 ± 0.38	65.51 ± 0.34
ProtoNet	NeurIPS’17	Metric	47.05 ± 0.35	68.56 ± 0.16	46.11 ± 0.39	70.07 ± 0.34
RelationNet	CVPR’18	Metric	51.52 ± 0.37	66.76 ± 0.30	54.37 ± 0.44	71.93 ± 0.35
CovaMNet	AAAI’19	Metric	51.59 ± 0.36	67.65 ± 0.32	51.92 ± 0.40	69.76 ± 0.34
DN4	CVPR’19	Metric	54.47 ± 0.36	72.15 ± 0.29	56.07 ± 0.38	75.75 ± 0.31
CAN	NeurIPS’19	Metric	55.88 ± 0.38	70.98 ± 0.30	55.96 ± 0.42	70.52 ± 0.35

ResNet12

Method	Venue	Type	miniImageNet		tieredImageNet
Method	Venue	Type	1-shot	5-shot	1-shot	5-shot
Baseline	ICLR’19	Fine-tuning	56.39 ± 0.36	76.18 ± 0.27	-	-
Baseline++	ICML’19	Fine-tuning	56.75 ± 0.38	66.36 ± 0.29	65.95 ± 0.42	82.25 ± 0.31
RFS-simple	ECCV’20	Fine-tuning	61.65 ± 0.35	78.88 ± 0.25	70.55 ± 0.42	84.74 ± 0.29
RFS-distill	ECCV’20	Fine-tuning	-	-	-	-
SKD-GEN0	arXiv’20	Fine-tuning	66.40 ± 0.36	83.06 ± 0.24	-	-
SKD-GEN1	arXiv’20	Fine-tuning	67.35 ± 0.37	83.31 ± 0.24	-	-
NegCos	ECCV’20	Fine-tuning	60.60 ±	78.80 ±	70.15 ±	84.94 ±
RENet	ICCV’21	Fine-tuning	64.81 ± 0.37	79.90 ± 0.27	70.14 ± 0.43	82.70 ± 0.31
Versa	NeurIPS’18	Meta	55.71 ± 0.40	70.05 ± 0.31	57.14 ± 0.43	75.48 ± 0.34
R2D2	ICLR’19	Meta	59.52 ± 0.39	74.61 ± 0.30	65.07 ± 0.44	83.04 ± 0.30
LEO	ICLR’19	Meta	53.58 ± 0.39	68.24 ± 0.32	-	-
MTL	CVPR’19	Meta	-	-	-	-
ANIL	ICLR’20	Meta	52.77 ± 0.40	68.11 ± 0.34	55.65 ± 0.44	73.53 ± 0.35
BOIL	ICLR’21	Meta	58.87 ± 0.38	72.88 ± 0.29	64.66 ± 0.44	80.38 ± 0.31
ProtoNet	NeurIPS’17	Metric	58.61 ± 0.38	75.02 ± 0.28	62.93 ± 0.43	83.30 ± 0.29
RelationNet	CVPR’18	Metric	55.22 ± 0.39	69.25 ± 0.31	-	-
CovaMNet	AAAI’19	Metric	56.95 ± 0.37	71.41 ± 0.66	58.49 ± 0.42	76.34 ± 0.37
DN4	CVPR’19	Metric	58.68 ± 0.37	74.70 ± 0.28	64.41 ± 0.42	82.59 ± 0.30
CAN	NeurIPS’19	Metric	59.82 ± 0.38	76.54 ± 0.29	70.46 ± 0.43	84.50 ± 0.30

ResNet18

Method	Venue	Type	miniImageNet		tieredImageNet
Method	Venue	Type	1-shot	5-shot	1-shot	5-shot
Baseline	ICLR’19	Fine-tuning	54.11 ± 0.35	74.44 ± 0.29	64.65 ± 0.41	82.73 ± 0.29
Baseline++	ICML’19	Fine-tuning	-	-	-	-
RFS-simple	ECCV’20	Fine-tuning	61.65 ± 0.37	76.60 ± 0.28	69.14 ± 0.42	83.21 ± 0.31
RFS-distill	ECCV’20	Fine-tuning	-	-	-	-
SKD-GEN0	arXiv’20	Fine-tuning	66.18 ± 0.37	82.21 ± 0.24	70.00 ± 0.57	84.70 ± 0.41
SKD-GEN1	arXiv’20	Fine-tuning	66.70 ± 0.37	82.60 ± 0.24
NegCos	ECCV’20	Fine-tuning	60.99 ±	76.30 ±	68.36 ±	83.77 ±
RENet	ICCV’21	Fine-tuning	62.86 ± 0.37	-	71.53 ± 0.43	-
Versa	NeurIPS’18	Meta	55.08 ± 0.39	69.16 ± 0.30	57.30 ± 0.45	75.67 ± 0.38
R2D2	ICLR’19	Meta	58.36 ± 0.38	75.69 ± 0.29	64.73 ± 0.44	83.40 ± 0.31
LEO	ICLR’19	Meta	57.51 ± 0.39	69.33 ± 0.38	64.02 ± 0.44	78.89 ± 0.33
MTL	CVPR’19	Meta	-	-	-	-
ANIL	ICLR’20	Meta	52.96 ± 0.40	65.88 ± 0.33	55.81 ± 0.46	73.53 ± 0.37
BOIL	ICLR’21	Meta	57.85 ± 0.40	70.84 ± 0.29	60.85 ± 0.45	77.74 ± 0.38
ProtoNet	NeurIPS’17	Metric	58.48 ± 0.39	75.16 ± 0.29	63.50 ± 0.48	82.51 ± 0.30
RelationNet	CVPR’18	Metric	53.98 ± 0.37	71.27 ± 0.31	-	-
CovaMNet	AAAI’19	Metric	55.83 ± 0.39	70.97 ± 0.33	54.12 ± 0.47	73.51 ± 0.55
DN4	CVPR’19	Metric	57.92 ± 0.37	75.50 ± 0.28	64.83 ± 0.42	82.77 ± 0.30
CAN(!re-run,h2=11!)	NeurIPS’19	Metric	60.78 ± 0.40	75.05 ± 0.29	71.70 ± 0.43	84.61 ± 0.37