Source-linked AI summary
Editable Neural Networks
Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitriy Pyrkin, Sergei Popov, Artem Babenko
TL;DR
Neural-network mistakes can be costly, yet correcting one input can damage predictions elsewhere and existing workarounds are expensive or brittle. The paper proposes Editable Training, which prepares models for gradient-based editing, and reports advantages over baselines across image classification and machine translation.
Problem
Correcting a neural-network mistake on one sample without influencing other samples is difficult, while retraining is expensive and lookup-table overrides are brittle.
Method
Editable Training is a model-agnostic training procedure that prepares parameters for efficient gradient-based editing through an editor satisfying correction constraints.
Results
The method demonstrates advantages over reasonable baselines on large-scale image classification and machine translation tasks.
Takeaways & Limitations
Editable Training enables model mistakes to be corrected while preserving overall performance and can make editing more practical.
Takeaways & Limitations
The approach depends on balancing edit-related objectives against the original task objective; large weights can sacrifice some model performance.
Abstract
from arXiv · showhide
These days deep neural networks are ubiquitously used in a wide range of tasks, from image classification and machine translation to face identification and self-driving cars. In many applications, a single model error can lead to devastating financial, reputational and even life-threatening consequences. Therefore, it is crucially important to correct model mistakes quickly as they appear. In this work, we investigate the problem of neural network editing $-$ how one can efficiently patch a mistake of the model on a particular sample, without influencing the model behavior on other samples. Namely, we propose Editable Training, a model-agnostic training technique that encourages fast editing of the trained model. We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks.
1 INTRODUCTION
The paper frames fast, localized correction of neural-network mistakes as an important but difficult problem, then proposes Editable Training to prepare models for efficient editing without harming overall performance.
- Single neural-network errors can carry financial, reputational, or life-threatening consequences in applications such as translation and self-driving vehicles.
- Changing predictions for one input is difficult because most architectures make each prediction depend on all model parameters, risking degraded performance elsewhere.
- Retraining from scratch is computationally expensive, while lookup-table overrides are not robust to viewpoint changes or paraphrased inputs.
- The study identifies fast neural-network editing as an under-attended practical problem and proposes Editable Training as its solution.
- Editable Training uses model-agnostic training so trained parameters can later be edited efficiently to correct model errors.
- The method is evaluated on large-scale image classification and machine translation, with results reported as advantageous over existing baselines.
2 RELATED WORK
Editable Neural Networks are positioned within meta-learning, continual-learning, and adversarial-training literature while addressing efficient correction rather than those neighboring objectives.
- Meta-learning: Editable Neural Networks fit the meta-learning paradigm by learning models that allow effective patching, a problem the authors say prior meta-learning work had not addressed.
- Catastrophic forgetting: Efficient neural-network patching resembles overcoming catastrophic forgetting because corrections should not alter behavior on other samples, but the setup is not sequential.
- Adversarial training: Editable Training differs from adversarial training because it enables efficient correction on selected samples, whereas adversarial training targets robustness to input perturbations.
3 EDITING NEURAL NETWORKS
The paper formalizes editing as parameter adjustment under a correction constraint, requiring reliability, locality, and efficiency, and trains models to support this editor through differentiable optimization.
- Editing objective: Editing applies an editor function ˆθ=Edit(θ, l_e) that adjusts parameters to satisfy the constraint l_e(ˆθ) ≤0.
- Editing objective: For classification, the editing constraint ensures the desired label has the highest log-probability for the selected input.
- Editor requirements: A practical editor must be reliable, local, and efficient in its effects, runtime, and memory use.
- Gradient-descent editor: The gradient-descent editor shifts parameters by −α∇_θl_e(θ) for up to k steps until the editing constraint is satisfied.
- Gradient-descent editor: Momentum and adaptive optimizers can augment gradient editing; RProp, SignSGD, and RMSProp were observed to produce more robust updates that improve locality.
- Editable Training: Editable Training learns parameters prepared for editing by backpropagating through the differentiable gradient-descent editor.
- Editable Training: Its objective combines the base task loss with edit and locality terms, where locality minimizes KL divergence between original and edited predictions.
- Editable Training: Large edit and locality weights can sacrifice task performance, while sufficiently small weights preserved the main objective in the reported experiments, attributed to overparameterization.
4 EXPERIMENTS
Experiments evaluate Editable Training and alternative editing procedures across CIFAR-10, ImageNet natural adversarial examples, and machine translation. Results indicate improved locality and practical editing while preserving task performance in several settings.
- Editor functions: RProp and RMSProp significantly outperform standard Gradient Descent in editor locality, while Momentum and Adam show smaller gains.The comparison uses drawdown, success rate, and the number of editing steps as evaluation measures.
- CIFAR-10: Editable Training reduces drawdown without significantly affecting CIFAR-10 test error, and editing Chain 3 alone is almost as effective as editing the whole model.Editing only Chain 3 makes training approximately 2.5 times slower than baseline training, while Editable+CNP is nearly as effective as gradient-based editors.
- Edited-model analysis: Editable Training does not always follow baseline class-locality patterns: its edit clusters depend on both original and target labels and have a highly interlinked middle region.The authors associate this structure with lower drawdown and hypothesize that edits affect same-class objects less strongly.
- Edited-model analysis: Editable Training provides evidence of more capacity-efficient edit representations, based on explained variance analysis of edit descriptors.The analysis uses truncated SVD to examine how many components explain given proportions of variance.
- Realistic edit tasks: On natural adversarial examples, random-target Editable Training significantly reduces drawdown, while matching target-class rank distributions improves locality further.Training on 6,500 actual natural adversarial examples performs no better than matching the target-rank distribution; in translation, Editable Training matches baseline quality with less than half the drawdown.
5 CONCLUSION
The paper addresses efficient correction of neural-network mistakes and introduces Editable Training for gradient-based model editing. Experiments show advantages over reasonable baselines on large-scale image classification and machine translation.
- The study addresses efficient correction of neural-network mistakes, an important task for deep-learning practitioners.
- Editable Training produces models that support gradient-based editing to correct model behavior.
- Editable Training outperforms reasonable baselines on large-scale image classification and machine translation tasks.