Teach neural networks when to say "I don't know" with

Teach neural networks when to say "I don't know" with HALO-Loss.

In the realm of neural networks, the challenge of distinguishing between uncertainty and false confidence is crucial.

April 14, 20263 min readMachine Learning

Neural networks are bad at admitting ignorance, and that is a problem we should have taken more seriously a long time ago. The HALO-Loss approach directly addresses this by giving models a built-in, mathematically sound way to say "I don't know." It is not a flashy trick or a band-aid; it is a structural fix to a fundamental flaw in how we train classifiers. The fact that it does this without sacrificing accuracy is what makes it worth your attention.

The practical implications are straightforward. Standard Cross-Entropy loss pushes features away from the origin indefinitely, which means the model has no natural place to represent uncertainty. HALO flips that by bounding confidence to a finite distance and attaching an "Abstain Class" right at the origin. For anyone working on safety-critical systems, this is the difference between a model that quietly fails and one that raises its hand. The numbers here are not marginal either: calibration error drops from roughly 8% to 1.5%, and false positives on far out-of-distribution data are cut by more than half. That is not a trade-off; that is an upgrade on both fronts.

What stands out is that this is not buried in a research paper behind paywalls. The author has open-sourced the code and written a breakdown of the math, including the practical pitfalls like dealing with high-dimensional Gaussian "soap bubbles." That matters because it lowers the barrier to entry. You do not need to be a machine learning researcher to test this on your own data. You can run it, see if your network's overconfidence drops, and decide for yourself. That is how progress should feel: accessible, measurable, and direct.

The real takeaway is that this is a viable path toward safer models without the usual tax. If you are training a CLIP-style model or any classifier where a wrong answer is expensive, HALO gives you a rejection threshold that is not bolted on after the fact. It is native to the training process. That is a meaningful step forward, and it is available now. Try it, stress-test it, and see if your model finally learns when to stay quiet.

From Machine Learning

Current neural networks have a fundamental geometry problem: If you feed them garbage data, they won't admit that they have no clue. They will confidently hallucinate. This happens because the standard Cross-Entropy loss requires models to push their features "infinitely" far away from the origin to reach a loss of 0.0 which leaves the model with a jagged latent space. It literally leaves the model with no mathematically sound place to throw its trash.
I've been working on a "fix" for this, and as a result I just open-sourced the HALO-Loss.

The hidden risk in AI plant identification: closed-set models can't say "I don't know.

I’ve been building an open-sourced handheld device for field identification of edible and toxic plants wild plants, and fungi, running entirely on device. Early on I trained specialist YOLO models on iNaturalist research grade data and hit 94-96% accuracy across my target species. Felt great, until I discovered a problem I don’t see discussed enough on this sub. YOLO’s closed set architecture has no concept of “I don’t know.” Feed it an out of distribution image and it will confidently classify it as one of its classes at near 100% confidence. In most CV cases this can be annoyance. In foraging, it’s potentially lethal. I tried confidence threshold fine-tuning at first, doesn’t work. The confidence scores on OOD inputs are indistinguishable from in-distribution predictions because the softmax output is normalized across a closed-set. There’s no probability mass allocated to “none of the above”. My solution was to move away from YOLO entirely (the use case is single shot image classification, not a video stream) and build a layered OOD detection pipeline. - EfficientNet B2 specialist models: Mycologist, berries, and high value foraging instead of one monolithic detector. - MobileNetV3 small domain router that directs inputs to appropriate specialist model or rejects it before classification. - Energy scoring on raw logits pre softmax to detect OOD inputs. Energy scores separate in-distribution from OOD far more cleanly than softmax confidence. - Ensemble disagreement across the three specialists as a secondary OOD signal. - K+1 “none the above” class retrained into each specialist model. The whole pipeline needs to run within the Hailo 8L’s 13 TOPS compute budget on a battery powered handheld. All architecture choices are constrained by real inference latency, not just accuracy on desktop. Curious if others have run into this closed-set confidence problem in safety-critical applications and what approaches you’ve taken? The energy scoring method (from the “Energy-based Out-of-Distribution Detection” paper by Liu et al.) has been the single biggest improvement over native confidence thresholding. submitted by /u/Adebrantes [link] [comments]