Gauge-Fixing the Forward-Forward Objective: A Whitened Goodness Derived from a Likelihood-Ratio Account

arXiv:2607.12501 · cs.LG, eess.IV, stat.ML · Submitted 2026-07-14 · Read on arXiv

Paolo Giannitrapani

cs.LG, eess.IV, stat.ML

Submitted: 2026-07-14

Code: https://github.com/fastai/imagenette

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 15× in this paper · explained in 2 episodes
• AUC — 10× in this paper · explained in 2 episodes
• Inflation — 9× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Anisotropy — 4× in this paper · explained in 4 episodes
• Calibration — 4× in this paper · explained in 9 episodes
• Generalization — 4× in this paper · explained in 14 episodes
• Adam — 3× in this paper · explained in 3 episodes
• Perceptual Alignment — 3× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Monotonicity — 2× in this paper · explained in 2 episodes
• Sufficiency — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Division of Labor — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Gauge Fixing — 1× in this paper · explained in 2 episodes
• Linear Probe — 1× in this paper · explained in 3 episodes
• Localization — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 3 episodes
• ReLU Network — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Scale-Invariance — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Partial-Reference IQA Based on Hermite-Gauss Structural Prediction and Texture Deviation
• The Forward-Forward Algorithm: Some Preliminary Investigations
• Covariance-Aware Goodness for Scalable Forward-Forward Learning
• Layer Collaboration in the Forward-Forward Algorithm
• Representation Learning with Contrastive Predictive Coding
• The Predictive Forward-Forward Algorithm
• In Search of Goodness: Large Scale Benchmarking of Goodness Functions for the Forward-Forward Algorithm
• The Cascaded Forward Algorithm for Neural Network Training
• Activation Learning by Local Competitions

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