Comments (2)
Have you done it? Could you share how you do it?
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Sure. You will have to change the ig4_rbf_vs_inception.ipynb to include a CNN. They already included a CNN (all_CNN_c.py) which you can just use. In order to use your own CNN you have to change the inference function in all_CNN_c. The structure is just a basic tensorflow graph, you can look up the structure in your online tutorial of choice.
You may also have a look at my fork of the project in which I changed the code in order to fit my CNN.
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Related Issues (20)
- Pytorch implementation HOT 26
- Perturbation to find influential features in training data HOT 6
- Need help: what does `set_params_op` do? HOT 2
- How can we implement this method on LSTM HOT 4
- Why the hessian vector product is calculated by mini-batch? HOT 3
- Maybe a bug: retrain without re-initialization? HOT 5
- Error in influence calculation for spam experiment HOT 2
- Upweighting a training point vs Perturbing a training input HOT 2
- Mismatch between IHVP computation in code and paper? HOT 1
- why does inverse_hvp / scale after the iteration in get_inverse_hvp_lissa HOT 1
- Minor error in documentation HOT 3
- Typo in paper HOT 1
- Local issue running Hospital Readmission notebook HOT 3
- Adapting this approach to regressions tasks not just classification HOT 1
- Influence value for RNN models HOT 1
- Can't understand what function "get_inverse_hvp_lissa" do HOT 1
- How much system memory is required in this source ? HOT 2
- How is the damping term determined? HOT 2
- How to Computing Influence Function HOT 5
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