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Image Classification on CIFAR-100 (val)

94.11AccuracyDoRA
76.346880.958485.5790.1816Jul 19, 2019Aug 28, 2020Oct 8, 2021Nov 18, 2022Dec 29, 2023Feb 7, 2025Mar 20, 2026

Evaluation results

MethodLinks
DoRAModel=ViT Large, Params (%)=0.552025.1094.11————————
LoRAModel=ViT Large, Params (%)=0.532025.1094.06————————
PiSSAModel=ViT Large, Params (%)=0.532025.1093.99————————
StelLAModel=ViT Large, Params (%)=0.542025.1093.92————————
DoRAModel=ViT Base, Params (%)=0.752025.1092.78————————
StelLAModel=ViT Base, Params (%)=0.732025.1092.72————————
LoRAModel=ViT Base, Params (%)=0.722025.1092.6————————
PiSSAModel=ViT Base, Params (%)=0.722025.1092.28————————
Full fine-tuningFine-tuning regime=Full2022.0391.8————————
Baseline with LoRAPre-training=ViT-MAE (pre-trained on ImageNet), Total Params (M)=309.72, Trainable Params (M)=6.42, Training Time (min)=116.50±12.61, Memory (GB-VRAM)=57.75±0.59, FLOPs (G)=60.94, Throughput (img/s)=88.78±0.49, Inference Time (s)=1.49±0.262026.0391.8————————
Learnable Memory TokensMemory cells=102022.0391.1————————
Learnable Memory TokensMemory cells=52022.0391————————
Learnable Memory TokensMemory cells=12022.0390.9————————
Learnable Memory TokensMemory cells=202022.0390.9————————
BaselinePre-training=ViT-MAE (pre-trained on ImageNet), Total Params (M)=303.40, Trainable Params (M)=303.40, Training Time (min)=49.31±5.37, Memory (GB-VRAM)=49.59±1.46, FLOPs (G)=59.70, Throughput (img/s)=119.11±1.19, Inference Time (s)=1.02±0.152026.0388.8————————
Head + Class token fine-tuningFine-tuning regime=Head + Class2022.0388.6————————
Head-only fine-tuningFine-tuning regime=Head Only2022.0385.8————————
CutMixBackbone=CAFormer-S12, Epochs=200, Batch size=1002025.1084.69————————
MergeMixBackbone=CAFormer-S12, Epochs=200, Batch size=1002025.1084.3————————
TransMixBackbone=CAFormer-S12, Epochs=200, Batch size=1002025.1083.7————————
DeiTBackbone=CAFormer-S12, Epochs=200, Batch size=1002025.1083.6————————
Reference Masking RegularizationBackbone=ViTb16, Pre-trained=ImageNet-1K, Adaptation Protocol=Linear Probing2024.0682.78————————
MixUpBackbone=CAFormer-S12, Epochs=200, Batch size=1002025.1081.64————————
Proposed-LMNoise rate=0.0, Selection=Best2023.0280.98————————
Proposed-LMNoise rate=0.0, Epoch selection=Best2023.0280.98————————
Proposed-MNoise rate=0.0, Epoch selection=Best2023.0280.61————————
Proposed-LNoise rate=0.0, Selection=Best2023.0280.44————————
Proposed-LNoise rate=0.0, Epoch selection=Best2023.0280.44————————
StandardBackbone=ViTb16, Pre-trained=ImageNet-1K, Adaptation Protocol=Linear Probing2024.0680.1————————
Proposed-LMNoise rate=0.0, Selection=Last2023.0279.71————————
Proposed-LMNoise rate=0.0, Epoch selection=Last2023.0279.71————————
Proposed-LNoise rate=0.1, Selection=Best2023.0279.45————————
Teacher BaselineTeacher=ResNet32x4, Student=ShuffleNet-V12023.1079.42————————
Teacher BaselineTeacher=ResNet32x4, Student=MobileNet-V22023.1079.42————————
TeacherTeacher architecture=resnet32x42024.1179.42————————
Proposed-MNoise rate=0.0, Epoch selection=Last2023.0279.39————————
Teacher BaselineTeacher=ResNet50, Student=MobileNet-V22023.1079.34————————
Proposed-LMNoise rate=0.1, Selection=Best2023.0279.32————————
DE-CENoise rate=0.0, Selection=Best2023.0279.13————————
DE-CENoise rate=0.0, Epoch selection=Best2023.0279.13————————
Proposed-LNoise rate=0.0, Selection=Last2023.0279.03————————
Proposed-LNoise rate=0.0, Epoch selection=Last2023.0279.03————————
LumiNetTeacher=WRN-40-2, Student=ShuffleNet-V12023.1078.95————————
LumiNetTeacher=ResNet32x4, Student=ShuffleNet-V12023.1078.88————————
Proposed-LMNoise rate=0.1, Selection=Last2023.0278.81————————
DYRNoise rate=0.0, Selection=Best2023.0278.64————————
DYRNoise rate=0.0, Epoch selection=Best2023.0278.64————————
TeacherArchitecture=ResNet-342021.0278.6————————
LOOKAHEADBackbone=ResNet-18, Optimizer=LOOKAHEAD2019.0778.34————————
DHKD + ReviewKDTeacher architecture=resnet32x4, Student architecture=resnet8x42024.1178.29————————
SGDBackbone=ResNet-18, Optimizer=SGD2019.0778.24————————
FCFDTeacher=ResNet32x4, Student=MobileNet-V22023.1078.2————————
FCFDTeacher=ResNet-32x4, Student=MobileNet-V22023.1078.2————————
AFDTeacher architecture=WRN-40-2, Student architecture=WRN-40-2, Architectural style=Same style2021.0278.13————————
FCFDTeacher=ResNet32x4, Student=ShuffleNet-V12023.1078.12————————
FCFDTeacher=ResNet-32x4, Student=ShuffleNet-V12023.1078.12————————
BDCKDTeacher=ResNet32x4, Student=MobileNet-V22023.1078.05————————
BDCKDTeacher=ResNet-32x4, Student=MobileNet-V22023.1078.05————————
TeacherTeacher Backbone=ResNet34, Student Backbone=ResNet182023.0578.05————————
SimKDTeacher architecture=resnet32x4, Student architecture=resnet8x4, Distillation manner=features2024.1178.04————————
DYRNoise rate=0.0, Selection=Last2023.0278.02————————
DYRNoise rate=0.0, Epoch selection=Last2023.0278.02————————
POLYAKBackbone=ResNet-18, Optimizer=POLYAK2019.0777.99————————
DHKD + ReviewKDTeacher architecture=WRN-40-2, Student architecture=WRN-16-22024.1177.97————————
Proposed-LMNoise rate=0.3x, Epoch selection=Best2023.0277.92————————
Proposed-MNoise rate=0.3x, Epoch selection=Best2023.0277.89————————
KDTeacher Backbone=ResNet34, Student Backbone=ResNet182023.0577.87————————
LumiNetTeacher=ResNet32x4, Student=MobileNet-V22023.1077.85————————
FCFDTeacher=WRN-40-2, Student=ShuffleNet-V12023.1077.81————————
FCFDTeacher=WRN-40-2, Student=ShuffleNet-V12023.1077.81————————
CRDTeacher architecture=WRN-40-2, Student architecture=WRN-40-2, Architectural style=Same style2021.0277.8————————
ReviewKDTeacher=ResNet32x4, Student=MobileNet-V22023.1077.78————————
ReviewKDTeacher=ResNet-32x4, Student=MobileNet-V22023.1077.78————————
ATTTeacher architecture=WRN-40-2, Student architecture=WRN-40-2, Architectural style=Same style2021.0277.78————————
FitNetTeacher architecture=WRN-40-2, Student architecture=WRN-40-2, Architectural style=Same style2021.0277.66————————
KDTeacher architecture=WRN-40-2, Student architecture=WRN-40-2, Architectural style=Same style2021.0277.63————————
RKDTeacher architecture=WRN-40-2, Student architecture=WRN-40-2, Architectural style=Same style2021.0277.62————————
OursTeacher=ResNet-32x4, Student=MobileNet-V22023.1077.55————————
Proposed-LMNoise rate=0.4x, Epoch selection=Best2023.0277.53————————
Proposed-MNoise rate=0.4x, Epoch selection=Best2023.0277.51————————
OursTeacher=ResNet-32x4, Student=ResNet-8x42023.1077.5————————
AFDTeacher architecture=ResNet-34, Student architecture=WRN-28-2, Architectural style=Different style2021.0277.47————————
ReviewKDTeacher=ResNet32x4, Student=ShuffleNet-V12023.1077.45————————
ReviewKDTeacher=ResNet-32x4, Student=ShuffleNet-V12023.1077.45————————
Proposed-LMNoise rate=0.4x, Epoch selection=Last2023.0277.32————————
BDCKDTeacher=ResNet-32x4, Student=ResNet-8x42023.1077.25————————
Single-CENoise rate=0.0, Epoch selection=Best2023.0277.23————————
BDCKDTeacher=ResNet32x4, Student=ShuffleNet-V12023.1077.2————————
BDCKDTeacher=ResNet-32x4, Student=ShuffleNet-V12023.1077.2————————
ATTTeacher architecture=ResNet-34, Student architecture=WRN-28-2, Architectural style=Different style2021.0277.2————————
Proposed-MNoise rate=0.4x, Epoch selection=Last2023.0277.19————————
Proposed-LNoise rate=0.1, Selection=Last2023.0277.15————————
ReviewKDTeacher=WRN-40-2, Student=ShuffleNet-V12023.1077.14————————
ReviewKDTeacher=WRN-40-2, Student=ShuffleNet-V12023.1077.14————————
StudentTeacher Backbone=ResNet34, Student Backbone=ResNet182023.0577.1————————
StudentTeacher Backbone=VGG11, Student Backbone=ResNet182023.0577.1————————
DKDTeacher=ResNet32x4, Student=MobileNet-V22023.1077.07————————
DKDTeacher=ResNet-32x4, Student=MobileNet-V22023.1077.07————————
DE-CENoise rate=0.3x, Epoch selection=Best2023.0277.07————————
Proposed-LMNoise rate=0.3x, Epoch selection=Last2023.0277.03————————