{"data":{"id":"ed9003fb-4c6f-452d-b5cc-f55b9d66f745","title":"Spurious Local Minima Provably Exist for Deep CNNs: Theory and Application","summary":"Researchers proved that spurious local minima (points where a neural network stops improving, but isn't at the best solution) definitely exist in deep CNNs (convolutional neural networks, which are commonly used for image recognition). They created a method to construct these problematic points mathematically and designed a new optimization algorithm (a step-by-step process for improving the network) that can escape from them, showing better accuracy than standard training methods like SGD or Adam on image datasets.","solution":"The source proposes a deterministic optimization method to escape local minima that is applicable to CNNs, ResNets, MLPs, and transformers. The authors report that experimental results on CIFAR-10, CIFAR-100, and ImageNet-1k datasets show their optimization method outperforms SGD or Adam in accuracy (by 0.27% on average) consistently across all tested architectures and datasets.","labels":["research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11301825","publishedAt":"2025-12-17T13:17:27.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2025-12-17T13:17:27.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}