Interestingly, CPCs capture both speaker identity and speech contents, as demonstrated by the good accuracies attained with a simple linear classifier, which also gets close to the oracle, fully supervised networks.
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Figure 6 shows that for 4 out of the 5 games performance of the agent improves significantly with the contrastive loss after training on 1 billion frames.
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CPC 76.9 80.1 91.2 87.7 96.8
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CPC 73.6
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CPC 48.7
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Despite being relatively domain agnostic, CPCs improve upon state-of-the-art by 9% absolute in top-1 accuracy, and 4% absolute in top-5 accuracy.
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We also found that not all the information encoded is linearly accessible. When we used a single hidden layer instead the accuracy increases from 64.6 to 72.5, which is closer to the accuracy of the fully supervised model.
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Ling. 1S-opt. 0.056 0.051 0.088 0.927 0.028 0.052 0.172 0.828 0.082 0.105 0.212 0.632
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Verb. 1S top-4 0.041 0.039 0.081 0.959 0.056 0.059 0.185 0.815 0.198 0.144 0.245 0.619
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Ling. 1S-opt. 0.058 0.066 0.135 0.878 0.064 0.068 0.220 0.674 0.125 0.165 0.270 0.492
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Verb. 1S top-4 0.054 0.057 0.144 0.896 0.065 0.051 0.209 0.763 0.203 0.189 0.284 0.455
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Among the methods for verbalizing probabilities directly, we observe that generating and evaluating multiple hypotheses improves calibration (see Figure 1), similarly to humans (Lord et al., 1985), and corroborating a similar finding in LMs (Kadavath et al., 2022).
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Ling. 1S-opt. 0.060 0.070 0.151 0.874 0.049 0.056 0.214 0.738 0.099 0.130 0.266 0.446
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