Learning Robust Visual-Semantic Embeddings
Table 8: Value of β and λ.
['Dataset  [ITALIC] attributes', 'AwA  [ITALIC] att', 'AwA  [ITALIC] w2v', 'AwA  [ITALIC] glo', 'CUB  [ITALIC] att', 'CUB  [ITALIC] w2v', 'CUB  [ITALIC] glo']
We have four parameters in our architecture: α,β,γ, and κ. We fix α=1.0, γ=0.1, κ=32.0 for all the experiments. Then we set λ=0.0 (no unsupervised-data adaptation inference), and perform cross-validation on the splitting set as suggested by [3,46] to determine β from {0.1,1.0}. Next, with chosen β, we perform cross-validation to choose λ from {0.1,1.0}.