discriminators matter
判別器很重要
using discriminators
使用判別器
discriminator design
判別器設計
discriminators identified
已識別的判別器
complex discriminators
複雜的判別器
discriminator features
判別器特徵
trained discriminators
經過訓練的判別器
discriminator performance
判別器性能
discriminators improved
改進的判別器
good discriminators
良好的判別器
the ai model used discriminators to distinguish between real and fake images.
人工智能模型使用判別器來區分真實與虛假圖像。
we trained the neural network with multiple discriminators for improved performance.
我們使用多個判別器來訓練神經網絡以提高性能。
the discriminator's role is to evaluate the generator's output in gans.
判別器的作用是在GAN中評估生成器的輸出。
adversarial training involves a generator and a discriminator competing against each other.
對抗訓練涉及生成器和判別器之間的競爭。
the discriminator provided valuable feedback to the generator during training.
在訓練過程中，判別器為生成器提供了寶貴的反饋。
we compared the performance of different discriminator architectures.
我們比較了不同判別器架構的性能。
the discriminator learned to identify subtle differences in the data.
判別器學會識別數據中的細微差異。
a well-designed discriminator is crucial for successful gan training.
一個設計良好的判別器對於成功的GAN訓練至關重要。
the discriminator's loss function guided the generator's learning process.
判別器的損失函數引導了生成器的學習過程。
we used a convolutional discriminator for image generation tasks.
我們使用卷積判別器進行圖像生成任務。
the discriminator's accuracy improved as the training progressed.
隨著訓練的進行，判別器的準確率得到了提高。
discriminators matter
判別器很重要
using discriminators
使用判別器
discriminator design
判別器設計
discriminators identified
已識別的判別器
complex discriminators
複雜的判別器
discriminator features
判別器特徵
trained discriminators
經過訓練的判別器
discriminator performance
判別器性能
discriminators improved
改進的判別器
good discriminators
良好的判別器
the ai model used discriminators to distinguish between real and fake images.
人工智能模型使用判別器來區分真實與虛假圖像。
we trained the neural network with multiple discriminators for improved performance.
我們使用多個判別器來訓練神經網絡以提高性能。
the discriminator's role is to evaluate the generator's output in gans.
判別器的作用是在GAN中評估生成器的輸出。
adversarial training involves a generator and a discriminator competing against each other.
對抗訓練涉及生成器和判別器之間的競爭。
the discriminator provided valuable feedback to the generator during training.
在訓練過程中，判別器為生成器提供了寶貴的反饋。
we compared the performance of different discriminator architectures.
我們比較了不同判別器架構的性能。
the discriminator learned to identify subtle differences in the data.
判別器學會識別數據中的細微差異。
a well-designed discriminator is crucial for successful gan training.
一個設計良好的判別器對於成功的GAN訓練至關重要。
the discriminator's loss function guided the generator's learning process.
判別器的損失函數引導了生成器的學習過程。
we used a convolutional discriminator for image generation tasks.
我們使用卷積判別器進行圖像生成任務。
the discriminator's accuracy improved as the training progressed.
隨著訓練的進行，判別器的準確率得到了提高。
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