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Last edited time
Jul 16, 2024 05:43 AM
😀
改变输入，改变架构
两个模型的输出并不会互相影响，也就是说二者是互相独立的
两个模型的输出并不会互相影响，也就是说二者是互相独立的

📝 主旨内容

改变输入

💡
多视角输入，通过3D点云生成多视角图片
notion image

改变架构

💡
卷积，KAN，KAN卷积，类重构先降维再升维（降维损失+重构损失）
 

结果

论文结果(投影)
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.990
0.894
0.986
0.989
0.980
0.916
0.951
0.916
0.986
0.886
0.949
AUPRO@30%
0.979
0.963
0.982
0.940
0.944
0.961
0.980
0.983
0.972
0.980
0.968
AUPRO@10%
0.937
0.892
0.947
0.890
0.838
0.885
0.940
0.948
0.918
0.941
0.914
AUPRO@5%
0.878
0.806
0.894
0.830
0.742
0.799
0.882
0.897
0.853
0.882
0.846
AUPRO@1%
0.469
0.402
0.486
0.450
0.380
0.397
0.463
0.490
0.453
0.463
0.445
复现投影
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.995
0.865
0.980
0.993
0.937
0.868
0.967
0.934
0.947
0.864
0.935
AUPRO@30%
0.980
0.959
0.982
0.943
0.922
0.962
0.980
0.983
0.969
0.981
0.966
AUPRO@10%
0.939
0.881
0.947
0.897
0.774
0.891
0.941
0.948
0.908
0.944
0.907
AUPRO@5%
0.881
0.791
0.895
0.841
0.671
0.808
0.884
0.896
0.839
0.888
0.839
AUPRO@1%
0.469
0.389
0.489
0.487
0.342
0.406
0.465
0.490
0.447
0.475
0.446
P-AUROC
0.997
0.988
0.999
0.972
0.977
0.992
0.998
0.998
0.997
0.996
0.991
cos_sim_2Dto3D
0.990
0.987
0.988
0.986
0.988
0.983
0.989
0.990
0.984
0.983
0.987
cos_sim_3Dto2D
0.820
0.876
0.833
0.791
0.880
0.821
0.808
0.839
0.849
0.854
0.837
复现投影(author checkpoint-50ep)
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.994
0.874
0.943
0.987
0.817
0.787
0.940
0.877
0.625
0.702
0.855
AUPRO@30%
0.980
0.952
0.982
0.945
0.929
0.932
0.980
0.982
0.952
0.967
0.960
AUPRO@10%
0.940
0.861
0.946
0.900
0.797
0.817
0.941
0.946
0.859
0.903
0.891
AUPRO@5%
0.882
0.766
0.893
0.846
0.695
0.714
0.882
0.892
0.758
0.817
0.815
AUPRO@1%
0.469
0.369
0.483
0.498
0.343
0.349
0.463
0.486
0.358
0.394
0.421
P-AUROC
0.997
0.985
0.998
0.972
0.979
0.984
0.998
0.998
0.993
0.990
0.989
cos_sim_2Dto3D
0.989
0.986
0.986
0.985
0.980
0.979
0.988
0.988
0.968
0.976
0.983
cos_sim_3Dto2D
0.799
0.856
0.803
0.759
0.786
0.772
0.776
0.795
0.617
0.757
0.772
复现投影更换数据集(author checkpoint-50ep)
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.990
0.903
0.980
0.990
0.973
0.898
0.948
0.916
0.972
0.858
0.943
AUPRO@30%
0.979
0.962
0.982
0.942
0.944
0.961
0.980
0.983
0.973
0.980
0.969
AUPRO@10%
0.939
0.890
0.947
0.895
0.837
0.885
0.941
0.949
0.921
0.940
0.914
AUPRO@5%
0.879
0.803
0.894
0.840
0.740
0.798
0.883
0.897
0.857
0.880
0.847
AUPRO@1%
0.466
0.403
0.486
0.488
0.376
0.396
0.466
0.494
0.455
0.469
0.450
P-AUROC
0.997
0.989
0.999
0.972
0.985
0.992
0.998
0.998
0.998
0.996
0.992
cos_sim_2Dto3D
0.990
0.987
0.988
0.986
0.988
0.984
0.989
0.990
0.984
0.983
0.987
cos_sim_3Dto2D
0.823
0.880
0.836
0.802
0.889
0.813
0.815
0.836
0.879
0.870
0.844
notion image
 
复现投影更换数据集(author checkpoint-250ep)
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.992
0.915
0.980
0.990
0.986
0.897
0.959
0.946
0.986
0.930
0.958
AUPRO@30%
0.979
0.965
0.982
0.941
0.952
0.964
0.980
0.983
0.975
0.982
0.970
AUPRO@10%
0.938
0.899
0.947
0.894
0.859
0.895
0.941
0.949
0.926
0.946
0.919
AUPRO@5%
0.879
0.818
0.893
0.838
0.768
0.813
0.883
0.897
0.865
0.892
0.855
AUPRO@1%
0.462
0.418
0.483
0.488
0.392
0.406
0.463
0.491
0.462
0.481
0.455
P-AUROC
0.997
0.990
0.999
0.971
0.987
0.993
0.998
0.999
0.998
0.997
0.993
cos_sim_2Dto3D
0.990
0.988
0.988
0.986
0.989
0.984
0.989
0.990
0.983
0.984
0.987
cos_sim_3Dto2D
0.823
0.891
0.825
0.799
0.892
0.816
0.814
0.822
0.882
0.873
0.844
notion image
0.0.
论文结果(卷积)
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.997
0.866
0.990
0.993
0.989
0.927
0.979
0.897
0.990
0.918
0.955
AUPRO@30%
0.979
0.965
0.982
0.941
0.948
0.969
0.982
0.983
0.977
0.981
0.971
AUPRO@10%
0.938
0.897
0.947
0.893
0.847
0.906
0.945
0.948
0.931
0.944
0.920
AUPRO@5%
0.880
0.813
0.894
0.834
0.756
0.820
0.891
0.896
0.872
0.889
0.855
AUPRO@1%
0.469
0.409
0.488
0.453
0.393
0.409
0.477
0.488
0.467
0.473
0.453
仅替换3Dto2D为卷积
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.992
0.877
0.981
0.996
0.996
0.938
0.982
0.923
0.954
0.881
0.952
AUPRO@30%
0.980
0.965
0.982
0.944
0.944
0.923
0.981
0.983
0.973
0.980
0.966
AUPRO@10%
0.941
0.897
0.947
0.898
0.898
0.777
0.943
0.948
0.919
0.941
0.911
AUPRO@5%
0.884
0.815
0.894
0.842
0.842
0.674
0.887
0.896
0.854
0.882
0.847
AUPRO@1%
0.473
0.410
0.486
0.477
0.477
0.344
0.468
0.491
0.458
0.469
0.455
P-AUROC
0.997
0.990
0.999
0.973
0.973
0.978
0.998
0.998
0.998
0.996
0.990
cos_sim_2Dto3D
0.990
0.987
0.988
0.986
0.986
0.988
0.989
0.990
0.984
0.983
0.987
cos_sim_3Dto2D
0.826
0.880
0.842
0.805
0.805
0.885
0.814
0.845
0.883
0.859
0.844
全替换为卷积(复现)
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.994
0.880
0.980
0.996
0.940
0.893
0.992
0.912
0.971
0.914
0.947
AUPRO@30%
0.979
0.963
0.982
0.945
0.923
0.968
0.981
0.983
0.975
0.982
0.968
AUPRO@10%
0.939
0.894
0.946
0.900
0.779
0.906
0.945
0.948
0.927
0.946
0.913
AUPRO@5%
0.881
0.809
0.893
0.843
0.678
0.826
0.890
0.897
0.864
0.893
0.847
AUPRO@1%
0.470
0.406
0.485
0.474
0.348
0.412
0.476
0.491
0.464
0.487
0.451
P-AUROC
0.997
0.989
0.998
0.973
0.977
0.992
0.998
0.998
0.998
0.996
0.992
cos_sim_2Dto3D
0.990
0.987
0.988
0.986
0.990
0.988
0.989
0.990
0.988
0.987
0.988
cos_sim_3Dto2D
0.825
0.881
0.845
0.805
0.884
0.827
0.817
0.843
0.884
0.858
0.847
全替换为encoder-decoder(复现)？？
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.997
0.841
0.983
0.989
0.938
0.890
0.966
0.891
0.950
0.915
0.936
AUPRO@30%
0.980
0.955
0.982
0.942
0.923
0.956
0.980
0.983
0.970
0.981
0.965
AUPRO@10%
0.940
0.871
0.947
0.895
0.777
0.879
0.940
0.948
0.911
0.944
0.905
AUPRO@5%
0.882
0.775
0.894
0.837
0.674
0.793
0.881
0.896
0.842
0.888
0.836
AUPRO@1%
0.473
0.382
0.487
0.471
0.343
0.399
0.461
0.489
0.447
0.475
0.443
P-AUROC
0.997
0.987
0.999
0.972
0.978
0.991
0.998
0.998
0.997
0.996
0.991
cos_sim_2Dto3D
0.990
0.986
0.988
0.986
0.988
0.983
0.989
0.990
0.984
0.982
0.987
cos_sim_3Dto2D
0.819
0.872
0.830
0.790
0.879
0.820
0.808
0.839
0.847
0.854
0.836
notion image
3D到2D替换为big-MLP
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.990
0.900
0.973
0.999
0.941
0.892
0.978
0.927
0.953
0.912
0.947
AUPRO@30%
0.980
0.966
0.982
0.944
0.924
0.960
0.980
0.982
0.970
0.981
0.967
AUPRO@10%
0.940
0.901
0.947
0.897
0.782
0.886
0.941
0.947
0.911
0.944
0.910
AUPRO@5%
0.882
0.818
0.894
0.841
0.681
0.798
0.882
0.894
0.843
0.888
0.842
AUPRO@1%
0.469
0.412
0.488
0.478
0.348
0.393
0.465
0.485
0.450
0.476
0.446
P-AUROC
0.997
0.991
0.999
0.973
0.978
0.991
0.998
0.998
0.997
0.997
0.992
cos_sim_2Dto3D
0.990
0.987
0.988
0.986
0.988
0.983
0.989
0.990
0.984
0.983
0.987
cos_sim_3Dto2D
0.815
0.881
0.828
0.786
0.881
0.821
0.806
0.837
0.851
0.855
0.836
notion image
3D到2D替换为重构
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.995
0.839
0.968
0.994
0.926
0.887
0.950
0.936
0.958
0.881
0.933
AUPRO@30%
0.980
0.948
0.982
0.943
0.919
0.962
0.979
0.983
0.970
0.981
0.965
AUPRO@10%
0.940
0.852
0.946
0.898
0.765
0.892
0.937
0.948
0.910
0.943
0.903
AUPRO@5%
0.882
0.751
0.893
0.843
0.662
0.810
0.875
0.897
0.842
0.886
0.834
AUPRO@1%
0.473
0.362
0.483
0.493
0.338
0.406
0.451
0.491
0.451
0.470
0.442
P-AUROC
0.997
0.984
0.998
0.972
0.976
0.992
0.998
0.998
0.997
0.996
0.991
cos_sim_2Dto3D
0.990
0.987
0.988
0.986
0.988
0.983
0.989
0.990
0.984
0.983
0.987
cos_sim_3Dto2D
0.796
0.808
0.808
0.762
0.833
0.791
0.785
0.814
0.829
0.813
0.804
notion image
3D到2D替换为重构(跳跃连接)
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.997
0.826
0.969
0.991
0.927
0.864
0.954
0.924
0.960
0.895
0.931
AUPRO@30%
0.979
0.954
0.982
0.942
0.922
0.965
0.979
0.983
0.970
0.981
0.966
AUPRO@10%
0.939
0.867
0.946
0.895
0.775
0.897
0.937
0.948
0.910
0.943
0.906
AUPRO@5%
0.881
0.766
0.893
0.839
0.671
0.814
0.874
0.895
0.841
0.886
0.836
AUPRO@1%
0.468
0.369
0.483
0.482
0.343
0.407
0.450 
0.489
0.449
0.474
0.441
P-AUROC
0.997
0.986
0.998
0.972
0.977
0.993
0.998
0.998
0.997
0.996
0.991
cos_sim_2Dto3D
0.990 
0.987
0.988
0.986
0.988
0.983
0.989
0.990 
0.984
0.983
0.987
cos_sim_3Dto2D
0.815
0.854
0.832
0.781
0.869
0.810 
0.798
0.835
0.850 
0.843
0.829
3D到2D替换为重构(跳跃连接)更换数据集
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
AUPRO@30%
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
AUPRO@10%
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
AUPRO@5%
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
AUPRO@1%
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
P-AUROC
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
cos_sim_2Dto3D
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
cos_sim_3Dto2D
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
notion image
 
notion image
3D到2D替换为KAN(128)更换数据集
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.997
0.863
0.975
0.988
0.974
0.858
0.964
0.899
0.974
0.891
0.938
AUPRO@30%
0.979
0.956
0.982
0.941
0.945
0.961
0.980
0.982
0.973
0.981
0.968
AUPRO@10%
0.938
0.870
0.946
0.894
0.841
0.885
0.939
0.947
0.921
0.944
0.913
AUPRO@5%
0.878
0.772
0.892
0.836
0.748
0.798
0.879
0.895
0.856
0.887
0.844
AUPRO@1%
0.463
0.372
0.483
0.477
0.382
0.394
0.460
0.487
0.454
0.473
0.445
P-AUROC
0.997
0.986
0.998
0.972
0.985
0.992
0.998
0.998
0.998
0.996
0.992
cos_sim_2Dto3D
0.990
0.988
0.988
0.986
0.989
0.984
0.989
0.990
0.984
0.985
0.987
cos_sim_3Dto2D
0.820
0.839
0.834
0.795
0.874
0.807
0.813
0.834
0.882
0.862
0.836
notion image
 
notion image
3D到2D替换为KAN(256)更换数据集
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.997
0.884
0.978
0.992
0.973
0.876
0.950
0.894
0.979
0.863
0.939
AUPRO@30%
0.980
0.956
0.982
0.942
0.946
0.961
0.980
0.983
0.973
0.981
0.968
AUPRO@10%
0.939
0.873
0.946
0.894
0.843
0.887
0.940
0.948
0.921
0.944
0.914
AUPRO@5%
0.881
0.776
0.893
0.837
0.750
0.803
0.880
0.896
0.857
0.888
0.846
AUPRO@1%
0.471
0.380
0.483
0.481
0.384
0.400
0.460
0.491
0.454
0.481
0.449
P-AUROC
0.997
0.987
0.999
0.972
0.985
0.992
0.998
0.998
0.998
0.996
0.992
cos_sim_2Dto3D
0.990
0.988
0.988
0.986
0.989
0.984
0.989
0.990
0.984
0.985
0.987
cos_sim_3Dto2D
0.821
0.853
0.835
0.799
0.878
0.809
0.815
0.836
0.881
0.867
0.839
notion image
 
notion image
优化器分离
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
AUPRO@30%
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
AUPRO@10%
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
AUPRO@5%
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
AUPRO@1%
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
P-AUROC
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
cos_sim_2Dto3D
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
cos_sim_3Dto2D
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
ㅤ
复现50epoch
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.989
0.903
0.981
0.990
0.973
0.898
0.952
0.914
0.971
0.856
0.943
AUPRO@30%
0.979
0.962
0.982
0.942
0.944
0.961
0.980
0.983
0.973
0.980
0.969
AUPRO@10%
0.939
0.890
0.947
0.895
0.837
0.885
0.941
0.949
0.921
0.940
0.914
AUPRO@5%
0.879
0.803
0.894
0.840
0.740
0.798
0.883
0.897
0.857
0.881
0.847
AUPRO@1%
0.467
0.402
0.486
0.488
0.376
0.396
0.466
0.494
0.456
0.469
0.450
P-AUROC
0.997
0.989
0.999
0.972
0.985
0.992
0.998
0.998
0.998
0.996
0.992
cos_sim_2Dto3D
0.990
0.987
0.988
0.986
0.988
0.984
0.989
0.990
0.984
0.983
0.987
cos_sim_3Dto2D
0.823
0.880
0.836
0.802
0.889
0.813
0.815
0.836
0.879
0.870
0.844
notion image
 
两组重构250epoch
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Potato
Rope
Tire
Mean
I-AUROC
0.992
0.910
0.975
0.995
0.983
0.886
0.977
0.903
0.978
0.901
0.950
AUPRO@30%
0.979
0.969
0.982
0.940
0.948
0.967
0.981
0.983
0.974
0.981
0.970
AUPRO@10%
0.939
0.910
0.946
0.894
0.849
0.902
0.943
0.948
0.922
0.942
0.920
AUPRO@5%
0.880
0.832
0.892
0.839
0.756
0.818
0.886
0.896
0.859
0.884
0.854
AUPRO@1%
0.468
0.420
0.483
0.485
0.382
0.405
0.470
0.490
0.460
0.474
0.454
P-AUROC
0.997
0.992
0.998
0.971
0.986
0.994
0.998
0.998
0.998
0.997
0.993
cos_sim_2Dto3D
0.989
0.987
0.987
0.985
0.988
0.983
0.988
0.989
0.982
0.982
0.986
cos_sim_3Dto2D
0.825
0.892
0.831
0.802
0.893
0.818
0.818
0.827
0.884
0.873
0.846
notion image
 

🤗 总结归纳

为什么会出现这种反直觉的现象

我同意3D到2D的映射理论上应该比2D到3D的映射更容易，因为给定3D位置确定2D位置比为2D位置"预测深度"（在某种意义上）更简单。
然而，3D特征(1152维)相对于2D特征(768维)具有更高的维度。因此，2D到3D的映射（这里指的是特征而非输入）基本上是一个扩展过程，而3D到2D的映射则是一个压缩过程。通常来说，压缩任务更具挑战性，我推测这就是为什么3D到2D的映射最终变得更加困难的原因。
特征降维的难点:
  1. 信息压缩:降维需要在保留关键信息的同时压缩数据,这需要复杂的算法来确定哪些特征是最重要的。
  1. 非线性关系:真实世界的数据通常包含复杂的非线性关系,简单的线性降维方法可能无法捕捉这些关系。
  1. 噪声干扰:降维过程中需要区分有用信息和噪声,这是一个具有挑战性的任务。
举例:主成分分析(PCA)是一种常用的线性降维方法,但它在处理非线性数据时效果不佳。为了解决这个问题,研究人员开发了更复杂的非线性降维技术,如t-SNE和自编码器。
特征升维相对容易:
  1. 信息添加:升维通常涉及添加新信息或转换现有特征,这在技术上较为简单。
  1. 灵活性:有多种方法可以进行特征升维,如多项式特征、核方法等。
  1. 过拟合风险:虽然升维可能导致过拟合,但这个问题可以通过正则化等技术来缓解。
举例:在卷积神经网络(CNN)中,卷积层通过增加特征图的数量来实现特征升维,这是一个相对直接的过程。

📎 参考文章

 
多模态工业异常检测BenchmarkNature润色指令——ChatGPT帮助我完成学术写作的三种方法
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