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Out-of-distribution (OoD) detectors, built on top of models trained on a single dataset (in-distribution, ID), should flag test-time inputs that don't belong to that distribution. Spike-like models replace standard...
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Out-of-distribution (OoD) detectors, built on top of models trained on a single dataset (in-distribution, ID), should flag test-time inputs that don't belong to that distribution. Spike-like models replace standard...
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64 Artefakteresnet10-cifar10/cross-entropy-baseline/T1_E1_S1987/model.safetensors
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safetensors · 18,7 MB · SHA-256 c55ff90d60e6…b86b · Hugging Face
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Herunterladen--- license: mit tags: - pytorch - image-classification - spiking-neural-network - out-of-distribution-detection - cifar10 - cifar100 datasets: - cifar10 - cifar100 library_name: pytorch --- # SpikingResNet Out-of-distribution (OoD) detectors, built on top of models trained on a single dataset (in-distribution, ID), should flag test-time inputs that don't belong to that distribution. Spike-like models replace standard ReLU activations with leaky integrate-and-fire (LIF) neurons on conventional ResNet backbones, operating at a single inference time step to achieve binary spike activations and substantially reduced arithmetic complexity, while remaining competitive with full-precision CNN baselines on OpenOOD benchmarks. Full code: [https://github.com/Alex-TLR/spikingResNet](https://github.com/Alex-TLR/spikingResNet) ## About the OoD experiments Two difficulties are evaluated: **near-OoD** (a semantically similar dataset, e.g. CIFAR-100 against a CIFAR-10 model) and **far-OoD** (a very different one, e.g. SVHN/MNIST). Detectors are scored by **AUROC** (higher is better) and **FPR95** (false-positive rate at 95% true-positive rate, lower is better). The experiments conform to the [OpenOoD benchmark](https://zjysteven.github.io/OpenOOD/). ## How this repository is organized The code includes the inference-only pipeline, the OoD benchmarks, and one folder per model "family" (architecture + dataset). Within each family, checkpoints are grouped by experiment (loss function + training time steps), then by expansion/seed: ``` spikingresnet/ ├── code/ <- inference and OoD code ├── checkpoints.csv <- every checkpoint in this repo, one row each └── <family>/<experiment>/T<T>_E<E>_S<seed>/ ├── config.json └── model.safetensors ``` 495 checkpoints across 7 families: - [`resnet10-cifar10`](#resnet10-cifar10) -- CIFAR10, 25 checkpoints - [`resnet18-cifar10`](#resnet18-cifar10) -- CIFAR10, 25 checkpoints - [`resnet4-cifar10`](#resnet4-cifar10) -- CIFAR10, 25 checkpoints - [`spikingresnet4-cifar10`](#spikingresnet4-cifar10) -- CIFAR10, 75 checkpoints - [`spikingresnet10-cifar10`](#spikingresnet10-cifar10) -- CIFAR10, 75 checkpoints - [`spikingresnet18-cifar10`](#spikingresnet18-cifar10) -- CIFAR10, 150 checkpoints - [`spikingresnet18-cifar100`](#spikingresnet18-cifar100) -- CIFAR100, 120 checkpoints Every experiment sweeps all 5 seeds (42, 1987, 1991, 2020, 20...
Source context: 0 downloads · 0 likes · Pipeline image-classification · Library pytorch · Repo dsluga/spikingresnet
