🌊Mish

NEW

A smooth, self-regularized, non-monotonic neural activation function that often improves accuracy over ReLU-like functions.

DESCRIPTION

Mish is a neural activation function defined as x * tanh(softplus(x)). Its smooth, non-monotonic shape can encourage better gradient flow and generalization than piecewise-linear functions like ReLU. Empirically, Mish has shown gains across CNNs and other deep networks, though results vary by task and architecture. It can be slightly more computationally expensive than ReLU due to tanh and softplus operations. Implementations are widely available in DL frameworks.

ALTERNATIVE DEFINITIONS

arXiv (Misra, 2019)

We propose Mish, a novel self regularized non-monotonic activation function.

Introduces Mish and emphasizes its self-regularization and non-monotonicity.

Source

PyTorch

Applies the Mish function element-wise: Mish(x) = x * tanh(softplus(x)).

Gives the exact functional form used in implementations.

Source

TensorFlow Addons

Mish activation: x * tanh(softplus(x)).

Concise formula from an official framework extension.

Source

RELATED TERMS

ELU Softplus Hyperbolic Tangent Activation Function Neural Network Deep Learning Backpropagation

COMPETING TERMS

Swish ReLU GELU

KNOWLEDGE AREAS

AI Mathematics Data Science

TARGET AUDIENCE

AI Engineer Data Scientist MLOps Engineer Software Developer

EXTERNAL SOURCES

• Wikipedia - Activation function
• arXiv - Original Mish paper and results
• PyTorch - Official PyTorch Mish module docs
• TensorFlow Addons - TensorFlow Addons Mish activation API

TOP QUESTIONS

What is Mish in neural networks?

Mish is an activation function defined as x * tanh(softplus(x)) that provides smooth, non-monotonic behavior and often improves model accuracy over ReLU.

Category: Definition

Why might Mish outperform ReLU?

Mish’s smoothness and soft saturation can stabilize gradients and improve generalization, reducing sharp activation transitions common in piecewise-linear functions like ReLU.

Category: Relevancy

When should Mish be used?

Use Mish when accuracy is paramount and extra compute cost is acceptable, especially in vision or deep architectures where smooth activations help optimization.

Category: Application

What are the downsides of Mish?

It’s costlier than ReLU due to tanh and softplus, and can have numerical stability issues for extreme inputs without careful implementation.

Category: Risks

How does Mish compare to Swish?

Both are smooth and non-monotonic; Mish uses tanh(softplus(x)) while Swish uses sigmoid. Empirical wins vary by task and architecture.

Category: Alternatives

Does Mish require different initialization?

Standard initializations like He or Xavier generally work; no special initialization is strictly required, but monitor for numerical stability.

Category: Requirements

Is Mish supported in major frameworks?

Yes, Mish is available in PyTorch (nn.Mish) and TensorFlow Addons, enabling easy integration into existing models.

Category: Practicality

What is a common mistake with Mish?

Using naive implementations that cause overflow in softplus or tanh at extreme values; prefer numerically stable library implementations.

Category: Mistakes

Is Mish still a hot topic?

Mish sparked interest as a strong ReLU alternative, and research continues comparing Mish with Swish and GELU on modern architectures.

Category: Topic