Papers › Trainable Highly-expressive Activation Functions

Trainable Highly-expressive Activation Functions

10 Jul 2024arXiv:2407.07564archive 2025-07-28

Irit Chelly, Shahaf E. Finder, Shira Ifergane, Oren Freifeld

Nonlinear activation functions are pivotal to the success of deep neural nets, and choosing the appropriate activation function can significantly affect their performance. Most networks use fixed activation functions (e.g., ReLU, GELU, etc.), and this choice might limit their expressiveness. Furthermore, different layers may benefit from diverse activation functions. Consequently, there has been a growing interest in trainable activation functions. In this paper, we introduce DiTAC, a trainable highly-expressive activation function based on an efficient diffeomorphic transformation (called CPAB). Despite introducing only a negligible number of trainable parameters, DiTAC enhances model expressiveness and performance, often yielding substantial improvements. It also outperforms existing activation functions (regardless whether the latter are fixed or trainable) in tasks such as semantic segmentation, image generation, regression problems, and image classification. Our code is available at https://github.com/BGU-CS-VIL/DiTAC.

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R2 bgu-cs-vil/ditac/regression_example/evaluation.py official repository ran fingerprinted MIT (permissive) · add0d777b27172fa · report
RMSE bgu-cs-vil/ditac/regression_example/evaluation.py official repository ran fingerprinted MIT (permissive) · 0088fd84030dee57 · report
compute_loss bgu-cs-vil/ditac/regression_example/evaluation.py official repository ran MIT (permissive) · 442198b32b497424 · report
get_fast_transform_grid_eval bgu-cs-vil/ditac/ditac/utils/fast_cpab.py official repository ran MIT (permissive) · 97af7e6c82e34298 · report
get_precision_mat bgu-cs-vil/ditac/ditac/utils/misc.py official repository ran MIT (permissive) · 1d87ab654455f87b · report
norm_data bgu-cs-vil/ditac/ditac/utils/misc.py official repository ran MIT (permissive) · 3b035cc02d994e08 · report
phi bgu-cs-vil/ditac/ditac/utils/misc.py official repository ran fingerprinted MIT (permissive) · 57b024a42d9d2520 · report
to bgu-cs-vil/ditac/ditac/utils/cpab_t.py official repository ran MIT (permissive) · c40a4a89747caf9e · report

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Image ClassificationImage GenerationSemantic Segmentationimage-classification

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