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32 lines
937 B
YAML
32 lines
937 B
YAML
# Objectosphere classifier (Dhamija et al., NeurIPS 2018).
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#
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# Extends the Entropic Open-Set loss with a logit-norm objective:
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# - Known samples: CE + hinge pushing ||logits|| >= xi
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# - Background samples: entropy maximisation + magnitude suppression (zeta * ||logits||^2)
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#
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# At inference time, flag unknown inputs with a norm threshold:
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# is_unknown = logit_norm < threshold
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# Choose the threshold from the validation set (e.g. 5th percentile of
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# known-class norms).
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model_type: ecd
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input_features:
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- name: feature_1
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type: number
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- name: feature_2
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type: number
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output_features:
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- name: label
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type: category
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loss:
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type: objectosphere
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background_class: 4 # adjust to match your vocabulary index
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xi: 10.0 # minimum logit norm for known-class samples
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zeta: 0.1 # weight for unknown-class magnitude suppression
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trainer:
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epochs: 50
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learning_rate: 0.001
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