Neural Network Fine-Tuning via Loss Landscape Pathfinding
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Solution Overview
Problem
Deep neural networks often rely on spurious attributes for predictions, leading to a lack of robustness and poor generalization beyond training distributions, as they tend to learn from sampling biases and hidden confounders in static datasets.
Innovation Solution
The Connectivity-Based Fine-tuning (CBFT) method iteratively adjusts the parameters of a neural network model to induce a more robust model that does not rely on spurious attributes by exploiting the non-linear relationship in the loss landscape between biased and unbiased models, altering mechanisms to learn different invariances and seeking a nonlinear path with a loss barrier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard training pipelines are used to train neural networks, then models achieve good in-distribution performance, but they rely on spurious attributes and fail to generalize out-of-distribution
Solution Approach 1:
The method performs preliminary action by first training a model using standard pipelines to achieve good in-distribution performance, then subsequently fine-tuning it with a different dataset to induce robustness. This two-stage approach allows the model to first learn effective representations and then adapt to be invariant to spurious attributes, resolving the contradiction between in-distribution performance and out-of-distribution generalization
Solution Approach 2:
The invention changes parameters by switching between different datasets during training - initially using a standard training dataset to achieve good performance, then fine-tuning with a different dataset that has different spurious correlations. This parameter change in the training data distribution forces the model to learn more robust, invariant features that generalize better out-of-distribution
2Productivity
If models learn from spurious attributes in training data, then training efficiency is improved, but robustness and generalization capability deteriorate
Solution Approach 1:
The method allows the model to initially learn from spurious attributes during standard training, which maintains training efficiency. Then, in a subsequent fine-tuning stage with a different dataset, the model is guided to unlearn these spurious correlations and learn robust invariant features. This preliminary action approach preserves training efficiency while ultimately achieving robustness
Solution Approach 2:
The invention converts the harmful effect of spurious attribute learning into a benefit by first allowing the model to learn these easy patterns (which improves initial training efficiency), then using a different dataset to expose and correct these biases. The spurious correlations learned initially serve as a baseline that can be systematically improved upon through fine-tuning
3Reliability
If fine-tuning seeks a nonlinear path with loss barrier in loss landscape, then robustness to spurious attributes is improved, but training complexity increases
Solution Approach 1:
The different training dataset acts as an intermediary that mediates between the initial biased model and the desired robust model. By introducing this intermediate training data with different spurious correlations, the method creates a controlled path through the loss landscape that guides the model from relying on spurious attributes to learning invariant features, managing the complexity of the transformation
Data Source
AI summary
Computer-implemented system and method are disclosed herein for fine tuning a neural network model. The method includes seeking, in a loss landscape, a nonlinear path with a loss barrier from a loss function associated with a first neural network model. The method further includes altering, in response to said seeking, one or more mechanisms of the first neural network model to induce a second neural network model.