‼️ Hyperparameters to set for implementing the paper
This file explains the hyperparameters to set in the scripts (definitions here)
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manualSeed : random seed for code reproducibility
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cls_weight : to decide how much weight to give to the classification loss, i.e.,
$\alpha_2$ in Eq. 6 of the paper (NOTE: cls_weight_unseen is not used in our work) -
nclass_all : total number of classes (including background)
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syn_num : number of features to synthesize per object class. An ablation study of its effect on model performance can be seen in Figure 2(b) of the paper
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val_every : validation rule
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cuda : setting the GPU flag
Regarding the WGAN network
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netG_name : generator network
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netD_name : discriminator network
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nepoch : total epochs to train
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ngh : linear layer neurons for generator network (ablations can be done to check for possible effects of network strength by varying the number of neurons)
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ndh : linear layer neurons for discriminator network (ablations can be done to check for possible effects of network strength by varying the number of neurons)
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lambda1 : for the gradient penalty trick of Wasserstein GANs (default value taken from the original NIPS 2017 paper)
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critic_iter : number of critic (discriminator) iterations per generator iteration for Wasserstein GANs (default value taken from the original NIPS 2017 paper)
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nz : dimension of the random noise vector
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gan_epoch_budget : random pick subset of features to train GAN
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lr : learning rate for training GAN
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lr_step : to decay the learning rate every lr_step epochs
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lz_ratio : mode seeking loss weight, i.e.,
$\alpha_3$ in our paper -
regressor_lamda :
$\alpha_4$ in our paper -
triplet_lamda :
$\alpha_5$ in our paper -
pretrain_regressor : The MLP for maintaining cyclic consistency
- dataset : name of dataset
- batch_size : batch size for training
- attSize : dimension of semantic vector
- resSize : dimension of visual embedding
- lr_cls : learning rate for training classifier
- pretrain_classifier : output of step 2
- class_embedding : class-attribute matrix
- dataroot : root directory for data
- testsplit/trainsplit : features extracted for test/train sets
- classes_split : seen/unseen split of classes
- outname : output results here