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Is it necessary to rebuild the model every train iteration?

Hi, thanks for the great work!

I noticed that you rebuild the meta-model every iteration (L129), and I was wondering if that is necessary?

RobustMW-Net/trainer.py

Lines 122 to 130 in cabea1f

for iters in range(args.iters):
adjust_learning_rate(optimizer_model, iters + 1)
model.train()
input, target = next(iter(train_loader))
input_var = to_var(input, requires_grad=False)
target_var = to_var(target, requires_grad=False)
meta_model = build_model()
meta_model.load_state_dict(model.state_dict())

Would it have any difference or negative impact if i were to just build the meta-model before the loop and reload the model's state_dict every iteration (L130) instead?

tabular data/ noisy instances

Hi,
thanks for sharing your implementation. I have two questions about it:

  1. Does it also work on tabular data?
  2. Is it possible to identify the noisy instances (return the noisy IDs or the clean set).

Thanks!

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