Bitwise Neural Networks vs Full-Precision Networks: Calibration and Uncertainty Estimation on CIFAR-100
Abstract
Abstract: Recently published methods enable training of bitwise neural networks which allow reduced representation of down to a single bit per weight. We present a method that exploits ensemble decisions based on multiple stochastically sampled network models to increase performance figures of bitwise neural networks in terms of classification accuracy at inference. Our experiments with the CIFAR-10 and GTSRB datasets show that the performance of such network ensembles surpasses the performance of the high-precision base model. With this technique we achieve 5.81\% best classification error on CIFAR-10 t
Research Question
How do bitwise neural networks with stochastic inference techniques perform in comparison to full-precision networks with Monte Carlo dropout in terms of calibration and uncertainty estimation metrics on CIFAR-100?
Verification Level
| Paper level | L2, Source-grounded claims | |
| Source-grounded claims | 8 | |
| Claim record source | parsed source sections |
Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.
Truth-Engine Gate Verdict
| Status | Falsified | |
| Gate | Gate 2 — Verification (formal proof or sandbox reproduction) | |
| Reason | [Gate 3 RED-TEAM FALSIFIED] avg_attack_score=9.5/10. COUNTEREXAMPLE_HUNTER(9.5):Fatal Mismatch between Research Goal and Verification Script. The Research Goal ; CITATION_AUDITOR(9.5):Critical Mismatch: The verification script computes a static dataset specificati; REPLICATION_ATTACKER(9.5):The verification script validates a trivial, hard-coded dataset specification (C | |
| Evaluated | 2026-06-13T11:49:12.343919+00:00 |
A claim in this record was tested against Gate 2 and failed: a counterexample was found, a proof did not type-check, or a reproduction attempt did not match the reported results. Evidence for the failure is attached to this record. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.
Quality Tier
| Tier | DOI grade | |
| Basis | Review score and verified-claim count meet DOI-grade public quality thresholds. |
Descriptive public triage only; this tier does not alter current publication or DOI behavior.
Quality Dimensions
| Evidence strength | MEDIUM | |
| Citation grounding | MEDIUM | |
| Uncertainty disclosure | MEDIUM | |
| Reproducibility status | HIGH |
Automated triage signals derived from public fields; not human peer review or independent validation.
Correction Record
| Status | CURRENT |
| Correction count | 0 |
| Manifest contract | paper-manifest-v1.1 |
| Correction contract | correction-record-v1 |
Public corrections are additive records. Current status does not claim the synthesis is error-free.
Provenance
| Publisher | Assignee Research |
| Public provenance | L4, External archival record |
| Report artifact | Available |
| External record | Registered |
| Claim lineage | 8 aggregate source-grounded claims |
| Review method | Automated multi-reviewer assessment |
| Quality guide | How to read scores, claims, manifests, and evidence links |
| Provenance contract | source-provenance-v1 |
| Note | Machine-generated synthesis of existing literature. Not primary research. |