Workshop: Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators

Dilated Convolution with Learnable Spacings: beyond bilinear interpolation

Ismail Khalfaoui Hassani · Thomas Pellegrini · Timothée Masquelier


Dilated Convolution with Learnable Spacings (DCLS) is a recently proposed variation of the dilated convolution in which the spacings between the non-zero elements in the kernel, or equivalently their positions, are learnable. Non-integer positions are handled via interpolation. Thanks to this trick, positions have well-defined gradients. The original DCLS used bilinear interpolation, and thus only considered the four nearest pixels. Yet here we show that longer range interpolations, and in particular a Gaussian interpolation, allow improving performance on ImageNet1k classification on two state-of-the-art convolutional architectures (ConvNeXt and Conv-Former), without increasing the number of parameters. The method code is based on PyTorch and is available at

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