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github.com/davidstutz/confidence-calibrated-adversarial-training @main

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1,075 symbols 2,887 edges 104 files 716 documented · 67% ● updated 6y ago★ 441 open issues

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⨍ Functions 916◇ Types & classes 159

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Core symbols most depended-on inside this repo

↓ 23 filesState

State of a model, including optional epoch and optimizer.

common/state.py

↓ 19 filesLog

Simple singleton log implementation with different drivers.

common/log.py

↓ 17 filesTrainingInterface

Training interface. Will only take care of the training and test step. This means that loading of model, optimizer, scheduler as …

common/train/training_interface.py

↓ 17 fileslog

Quick access to logger instance. :param message: message or variable to log :type message: mixed :param level: level, i.e. color …

common/log.py

↓ 14 filesObjective

Abstract objective.

attacks/objectives.py

↓ 11 filesAdversarialEvaluation

Evaluation on adversarial and clean examples.

common/eval/adversarial_evaluation.py

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tests/test_attacks_normal.py73 symbols

common/summary.py63 symbols

common/eval/adversarial_evaluation.py57 symbols

tests/test_eval.py48 symbols

common/torch.py45 symbols

attacks/initializations.py45 symbols

common/eval/corrupted_evaluation.py44 symbols

experiments/config/common.py39 symbols

common/datasets.py39 symbols

common/eval/distal_evaluation.py37 symbols

common/eval/clean_evaluation.py32 symbols

common/paths.py30 symbols

For agents

$ claude mcp add confidence-calibrated-adversarial-training \
  -- python -m otcore.mcp_server <graph>

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github.com/davidstutz/confidence-calibrated-adversarial-training @main

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