python sae/sae_gzsl.py -data AWA2/AWA1/CUB/SUN/APY -mode train -ld1 [LOWER BOUND OF VARIATION] -ld2 [UPPER BOUND OF VARIATION]
For testing, set mode to test and set ld1 (F->S) and ld2 (S->F) to the best values from the tables below.
The numbers below are class-averaged top-1 accuracies (see ZSLGBU paper for details).
| Dataset | ZSLGBU Results | Repository Results | |||
|---|---|---|---|---|---|
| F->S (W) | Lambda | S->F (W.T) | Lambda | ||
| CUB | 33.3 | 39.48 | 100 | 46.70 | 0.2 |
| AWA1 | 53.0 | 51.34 | 3.0 | 59.89 | 0.8 |
| AWA2 | 54.1 | 51.66 | 0.6 | 60.51 | 0.2 |
| aPY | 8.3 | 16.07 | 2.0 | 16.50 | 4.0 |
| SUN | 40.3 | 52.85 | 0.32 | 59.86 | 0.16 |
| Dataset | ZSLGBU Results | Repository Results | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| F->S (W) | Lambda | S->F (W.T) | Lambda | ||||||||
| U | S | H | U | S | H | U | S | H | |||
| CUB | 7.8 | 54.0 | 13.6 | 13.86 | 49.88 | 21.69 | 80 | 15.72 | 57.02 | 24.64 | 0.2 |
| AWA1 | 1.8 | 77.1 | 3.5 | 5.29 | 80.52 | 9.92 | 3.2 | 14.72 | 82.93 | 25.0 | 0.8 |
| AWA2 | 1.1 | 82.2 | 2.2 | 5.0 | 81.42 | 9.42 | 0.8 | 12.86 | 87.20 | 22.41 | 0.2 |
| aPY | 0.4 | 80.9 | 0.9 | 8.28 | 27.97 | 12.77 | 0.16 | 9.48 | 56.62 | 16.24 | 2.56 |
| SUN | 8.8 | 18.0 | 11.8 | 16.81 | 24.69 | 20.0 | 0.32 | 19.03 | 31.20 | 23.64 | 0.08 |
U -> Unseen Classes; S -> Seen Classes; H-> Harmonic Mean of the 2.
[1] Original MATLAB Code by Authors
[2] https://github.com/hoseong-kim/sae-pytorch