Document Text Classification on ANIMAL

Metric: Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	Jigsaw-ViT	89	No	Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Tr...	2022-07-25	Code
2	SURE	89	No	SURE: SUrvey REcipes for building reliable and r...	2024-03-01	Code
3	CLIPCleaner	88.85	No	CLIPCleaner: Cleaning Noisy Labels with CLIP	2024-08-19	Code
4	PSSCL	88.74	No	-	-	Code
5	SSR	88.5	No	SSR: An Efficient and Robust Framework for Learn...	2021-11-22	Code
6	BtR	88.5	No	Bootstrapping the Relationship Between Images an...	2022-10-17	Code
7	SPR	86.8	No	Scalable Penalized Regression for Noise Detectio...	2022-03-15	Code
8	Dynamic Loss	86.5	No	Dynamic Loss For Robust Learning	2022-11-22	Code
9	GNL	85.9	No	Partial Label Supervision for Agnostic Generativ...	2023-08-02	Code
10	C2MT	85.9	No	-	-	Code
11	InstanceGM with ConvNeXt	84.7	No	Instance-Dependent Noisy Label Learning via Grap...	2022-09-02	Code
12	InstanceGM	84.6	No	Instance-Dependent Noisy Label Learning via Grap...	2022-09-02	Code
13	Nested+Co-teaching (NCT)	84.1	No	Compressing Features for Learning with Noisy Lab...	2022-06-27	Code
14	PLC	83.4	No	Learning with Feature-Dependent Label Noise: A P...	2021-03-13	Code
15	InstanceGM with ResNet	82.3	No	Instance-Dependent Noisy Label Learning via Grap...	2022-09-02	Code
16	SELFIE	81.8	No	-	-	Code
17	CE + Dropout	81.3	No	Boosting Co-teaching with Compression Regulariza...	2021-04-28	Code
18	Nested Dropout	81.3	No	Boosting Co-teaching with Compression Regulariza...	2021-04-28	Code
19	Cross Entropy	79.4	No	Learning with Feature-Dependent Label Noise: A P...	2021-03-13	Code

#1Jigsaw-ViTSOTA
89
Accuracy· 2022-07-25
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer Code
#2SURE
89
Accuracy· 2024-03-01
SURE: SUrvey REcipes for building reliable and robust deep networks Code
#3CLIPCleaner
88.85
Accuracy· 2024-08-19
CLIPCleaner: Cleaning Noisy Labels with CLIP Code
#4PSSCL
88.74
Accuracy
No paperCode
#5SSRSOTA
88.5
Accuracy· 2021-11-22
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise Code
#6BtR
88.5
Accuracy· 2022-10-17
Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels Code
#7SPR
86.8
Accuracy· 2022-03-15
Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels Code
#8Dynamic Loss
86.5
Accuracy· 2022-11-22
Dynamic Loss For Robust Learning Code
#9GNL
85.9
Accuracy· 2023-08-02
Partial Label Supervision for Agnostic Generative Noisy Label Learning Code
#10C2MT
85.9
Accuracy
No paperCode
#11InstanceGM with ConvNeXt
84.7
Accuracy· 2022-09-02
Instance-Dependent Noisy Label Learning via Graphical Modelling Code
#12InstanceGM
84.6
Accuracy· 2022-09-02
Instance-Dependent Noisy Label Learning via Graphical Modelling Code
#13Nested+Co-teaching (NCT)
84.1
Accuracy· 2022-06-27
Compressing Features for Learning with Noisy Labels Code
#14PLCSOTA
83.4
Accuracy· 2021-03-13
Learning with Feature-Dependent Label Noise: A Progressive Approach Code
#15InstanceGM with ResNet
82.3
Accuracy· 2022-09-02
Instance-Dependent Noisy Label Learning via Graphical Modelling Code
#16SELFIE
81.8
Accuracy
No paperCode
#17CE + Dropout
81.3
Accuracy· 2021-04-28
Boosting Co-teaching with Compression Regularization for Label Noise Code
#18Nested Dropout
81.3
Accuracy· 2021-04-28
Boosting Co-teaching with Compression Regularization for Label Noise Code
#19Cross Entropy
79.4
Accuracy· 2021-03-13
Learning with Feature-Dependent Label Noise: A Progressive Approach Code