The test results were very disappointing. Seem that the Recurrent Models of Visual Attention the ability to locate targets, which mentioned in the paper.
I have created several datasets on the MNIST, including changing the object size in original data to 20x20, 14x14, etc. The trained model which has been trained 101 epochs and Train acc and Val acc reached 71.654%, 94.867%, respectively, is then used to test the new datasets.
The result as follows:Test on size_28x28 (without changing the data shape, to verify the feasibility of data shape change operation)
[] Test Acc: 1933/2000 (96.00% - 4.00%)
[] Test Acc: 3858/4000 (96.00% - 4.00%)
[] Test Acc: 5808/6000 (96.00% - 4.00%)
[] Test Acc: 7776/8000 (97.00% - 3.00%)
[*] Test Acc: 9749/10000 (97.00% - 3.00%)
Test on size_20x20:
[] Test Acc: 390/2000 (19.00% - 81.00%)
[] Test Acc: 761/4000 (19.00% - 81.00%)
[] Test Acc: 1107/6000 (18.00% - 82.00%)
[] Test Acc: 1469/8000 (18.00% - 82.00%)
[*] Test Acc: 1829/10000 (18.00% - 82.00%)
Test on size_14x14:
[] Test Acc: 257/2000 (12.00% - 88.00%)
[] Test Acc: 502/4000 (12.00% - 88.00%)
[] Test Acc: 744/6000 (12.00% - 88.00%)
[] Test Acc: 956/8000 (11.00% - 89.00%)
[*] Test Acc: 1167/10000 (11.00% - 89.00%)
The above results are basically the same as those I've seen with other deep learning (CNNs) models...
The test results were very disappointing. Seem that the Recurrent Models of Visual Attention the ability to locate targets, which mentioned in the paper.
I have created several datasets on the MNIST, including changing the object size in original data to 20x20, 14x14, etc. The trained model which has been trained 101 epochs and Train acc and Val acc reached 71.654%, 94.867%, respectively, is then used to test the new datasets.
The result as follows: Test on size_28x28 (without changing the data shape, to verify the feasibility of data shape change operation) [] Test Acc: 1933/2000 (96.00% - 4.00%) [] Test Acc: 3858/4000 (96.00% - 4.00%) [] Test Acc: 5808/6000 (96.00% - 4.00%) [] Test Acc: 7776/8000 (97.00% - 3.00%) [*] Test Acc: 9749/10000 (97.00% - 3.00%)
Test on size_20x20: [] Test Acc: 390/2000 (19.00% - 81.00%) [] Test Acc: 761/4000 (19.00% - 81.00%) [] Test Acc: 1107/6000 (18.00% - 82.00%) [] Test Acc: 1469/8000 (18.00% - 82.00%) [*] Test Acc: 1829/10000 (18.00% - 82.00%)
Test on size_14x14: [] Test Acc: 257/2000 (12.00% - 88.00%) [] Test Acc: 502/4000 (12.00% - 88.00%) [] Test Acc: 744/6000 (12.00% - 88.00%) [] Test Acc: 956/8000 (11.00% - 89.00%) [*] Test Acc: 1167/10000 (11.00% - 89.00%)
The above results are basically the same as those I've seen with other deep learning (CNNs) models...