Real or fake pair? D D G G tries to synthesize fake images that fool D D tries to identify the fakes Figure 2: Training a conditional GAN to predict aerial photos from maps. The discriminator, D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discrimina- Encoder-decoder U-Net Figure 3: Two choices for the architecture of the generator. The “U-Net” [34] is an encoder-decoder with skip connections be- tween mirrored layers in the encoder and decoder stacks. this strategy effective – the generator simply learned to ig- nore the noise – which is consistent with Mathieu et al. [27]. Instead, for our final models, we provide noise only in the form of dropout, applied on several layers of our generator at both training and test time. Despite the dropout noise, we υϝΠϯ:ˠυϝΠϯ9ˠυϝΠϯ: Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D D tries to identify the fakes Figure 2: Training a conditional GAN to predict aerial photos from maps. The discriminator, D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discrimina- Encoder-decoder U-Net Figure 3: Two choices for the architecture of the generator. The “U-Net” [34] is an encoder-decoder with skip connections be- tween mirrored layers in the encoder and decoder stacks. this strategy effective – the generator simply learned to ig- nore the noise – which is consistent with Mathieu et al. [27]. Instead, for our final models, we provide noise only in the form of dropout, applied on several layers of our generator at both training and test time. Despite the dropout noise, we X (5) ˆ X (6) Y (7) ˆ Y (8) Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D D tries to identify the fakes Figure 2: Training a conditional GAN to predict aerial photos from maps. The discriminator, D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discrimina- Encoder-decoder U-Net Figure 3: Two choices for the architecture of the gene “U-Net” [34] is an encoder-decoder with skip conne tween mirrored layers in the encoder and decoder stack this strategy effective – the generator simply learn nore the noise – which is consistent with Mathieu e Instead, for our final models, we provide noise on form of dropout, applied on several layers of our at both training and test time. Despite the dropout Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D D tries to identify the fakes Figure 2: Training a conditional GAN to predict aerial photos from maps. The discriminator, D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discrimina- Encoder-decoder Figure 3: Two choices for the architectur “U-Net” [34] is an encoder-decoder wit tween mirrored layers in the encoder and this strategy effective – the generator nore the noise – which is consistent wi Instead, for our final models, we prov form of dropout, applied on several la at both training and test time. Despite EAll = Edet(pi, p+ i , p− i ) + T −1 t=1 EClass or Reg t (pi) (4) X (5) ˆ X (6) Y (7) ˆ Y (8) 2. Concolusion Y (7) ˆ Y (8) F (9) G (10) DX (11) ˆ X (6) Y (7) ˆ Y (8) F (9) G (10) DX (11) Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D Encoder-deco Figure 3: Two choices f “U-Net” [34] is an enc tween mirrored layers in Real or fake pair? Positive examples Negative examples Real or fake pair? D D 'BLFPS3FBM QBJS Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D Figure 3: “U-Net” [ tween mirr Real or fake pair? Positive examples Negative examples Real or fake pair? D D Encoder-deco 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 ˆ X Y ˆ Y F G ( DX ( DY ( 'BLFPS3FBM QBJS Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D D tries to identify the fakes Figure 2: Training a conditional GAN to predict aerial photos from maps. The discriminator, D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discrimina- Encoder-deco Figure 3: Two choices “U-Net” [34] is an enc tween mirrored layers in this strategy effective nore the noise – which Instead, for our final form of dropout, appl at both training and te Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D D tries to identify the fakes Figure 2: Training a conditional GAN to predict aerial photos from maps. The discriminator, D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discrimina- tor observe an input image. where G tries to minimize this objective against an ad- versarial D that tries to maximize it, i.e. G ⇤ = Figure 3: “U-Net” [ tween mir this strate nore the n Instead, f form of d at both tr observe v Designin put, and distributi by the pr Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D D tries to identify the fakes Figure 2: Training a conditional GAN to predict aerial photos from maps. The discriminator, D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discrimina- tor observe an input image. where G tries to minimize this objective against an ad- versarial D that tries to maximize it, i.e. G ⇤ = Encoder-deco Figure 3: Two choices “U-Net” [34] is an enc tween mirrored layers in this strategy effective nore the noise – which Instead, for our final form of dropout, appl at both training and te observe very minor s Designing conditiona put, and thereby capt distributions they mo by the present work. Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D D tries to identify the fakes Figure 2: Training a conditional GAN to predict aerial photos from maps. The discriminator, D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discrimina- Figure 3: “U-Net” [ tween mir this strate nore the n Instead, f form of d at both tr 047 048 049 050 051 052 053 G (1 DX (1 DY (1 Real or fake pair? Positive examples Negative examples Real or fake pair? D D G G tries to synthesize fake images that fool D Encoder-decoder U-Net Figure 3: Two choices for the architecture of the generator. The “U-Net” [34] is an encoder-decoder with skip connections be- tween mirrored layers in the encoder and decoder stacks. X (5) ˆ X (6) Y (7) ˆ Y (8)