Er in the generator network. Table two. Output size with the layer within the generator network. Layer Layer Size Size Layer Layer Input Input 256 256 . ……. . … . ……. . … FC FC 4096 4096 Upsample 4 four Upsample Reshape Reshape 2 2 21024 1024 Scale 4 four Scale Upsample 0 0 Upsample 4 4 4 12 512 Upsample 5 five Upsample Scale 0 0 Scale four four four 12 512 Scale five 5 Scale Upsample 1 1 Upsample eight 8 eight 56 256 Conv ConvSize Size64 64 32 64 64 64 64 32 64 64 128 128 16 128 128 128 128 16 128 128 128 128 128 128 ure 2021, 11, x FOR PEER REVIEWThe discriminator will probably be able to differentiate the generated, reconstructed, and realThe discriminator will be in a position to differentiate the generated, reconstructed, and istic pictures as a great deal as you possibly can. Thus, the score for the original image need to be as realistic photos as much as you possibly can. As a result, the score for the original image really should higher as you can, as well as the scores for the generated and reconstructed images must be as be as higher as you possibly can, along with the scores for the generated and reconstructed images ought to low low as possible. Its Isophorone supplier structure is equivalent on the on the encoder, that the final two FCs be asas attainable. Its structure is equivalent to that to that encoder, except 9 of 19 that the final except with a with a size of generated at the finish and replaced with FC having a size of 1. The two FCssize of 256 are256 are generated at the end and replaced with FC having a size of 1. output is is accurate false, that is made use of to improve the image generation ability with the The outputtrue or or false, that is usedto improve the image generation potential of thenetwork, producing the generated image a lot more just like the facts are shown in network, producing the generated image far more like the real image.the actual image. The details are shown in Figure 6 and associated shown in are shown in Table 3. Figure six and related parameters Norigest Data Sheet areparametersTable 3.Figure six. Discriminator network.Figure six. Discriminator network. Table three. Output size in the layer inside the discriminator network.yer ze yer zeInput 128 128 three …… ……Conv 128 128 16 Downsample 3 eight 8 Scale 0 128 128 16 Scale 4 8 8 Downsample 0 64 64 32 ReducemeanScale 1 64 64 32 Scale_fcDownsample 1 32 32 64 FCAgriculture 2021, 11,9 ofFigure 6. Discriminator network.Table three. Output size of the layer in the discriminator network. Conv Scale 0 Downsample 0 Scale 1 DownsampleLayer Size Layer Layer Size Size LayerSizeInputTable 3. Output size in the layer within the discriminator network.128 128 three 128 128 16 128 128 16 64 64 32 64 64 32 32 32 64 Input Conv Scale 0 Downsample 0 Scale 1 Downsample 1 … … Downsample 3 Scale four Reducemean Scale_fc FC 128 128 3 128 128 16 128 128 16 64 64 32 64 64 32 32 32 64 8 3 1 ……. . . . . . Downsample 256 Scale8 8 256 four Reducemean256 Scale_fc 256 FC …… eight eight 256 eight eight 256 256 2563.two.three. Elements of Stage 2 Stage 2 is actually a VAE network consisting with the encoder (E) and decoder (D), which can be made use of Stage two distribution of consisting from the encoder (E) as well as the latent that is used to understand the is often a VAE network hidden space in stage 1 since decoder (D),variables occupy the to understand the distribution of hidden space in stage 1 because the latent variables occupy the entire latent space dimension. Both the encoder (E) and decoder (D) are composed of a whole latent space dimension. Each the encoder (E) and decoder (D) are composed of a completely connected layer. The structure is shown in Figure 7. The input in the model is usually a latent totally connected layer. The structur.
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