Papers › Evaluating the Performance of TAAF for image classification models
Evaluating the Performance of TAAF for image classification models
Bryn T Chatfield
In this paper, we present the results of testing a custom activation function, The Analog Activation Function (TAAF), on both the MNIST and CIFAR-10 datasets. TAAF is a novel activation function designed to improve the performance of neural networks by leveraging a unique mathematical formulation. We evaluate TAAF in a convolutional neural network (CNN) architecture and compare its performance against standard activation functions on MNIST and against ELU on CIFAR-10. Our results demonstrate that TAAF achieves a test accuracy of 99.39% on the MNIST dataset and 79.37% on the CIFAR-10 dataset. On MNIST, TAAF achieves a slightly higher test accuracy of 99.39%, surpassing standard activation functions. On CIFAR-10, TAAF achieves a significantly higher test accuracy of 79.37% compared to ELU's 72.06% in the same architecture, suggesting improved generalization capabilities. This paper establishes a solid performance baseline for TAAF across different image classification tasks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | The Analog Activation Function | Cross Entropy Loss | 0.5551 | #244 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | The Analog Activation Function | Parameters | 545100 | #244 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | The Analog Activation Function | Percentage correct | 82.06 | #244 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | The Analog Activation Function | Top 1 Accuracy | 82.06 | #244 of 265 | Archive leaderboard | report |
| Image Classification | MNIST | TAAF-CNN | Accuracy | 99.52% | #31 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | TAAF-CNN | Cross Entropy Loss | 0.0188 | #31 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | TAAF-CNN | Epochs | 35 | #31 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | TAAF-CNN | Percentage error | 0.48% | #31 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | TAAF-CNN | Trainable Parameters | 421642 | #31 of 81 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: TAAF
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