Эксперимент: Besov-Weighted Gaussian Persistence Regularizer (#1171)

{ "worked": false, "confidence": 9, "verdict": "Built the depthwise Gaussian scale-space and Besov-weighted DoG regularizer, including a finite-scale telescoping check and a fixed-seed toy classification experiment. The reconstruction identity held to 1.19e-7, but the regularizer produced no validation-accuracy improvement: both baseline and idea reached 1.0 throughout, while the idea added loss and computation. With the stated indexing and positive s, larger j corresponds to coarser scales, so the weighting does not directly emphasize fine-scale high frequencies as described.", "metrics": { "baseline": "Validation accuracy [1.0, 1.0, 1.0]; logged train loss [6.78e-7, 5.59e-9, 0.0]", "idea": "Validation accuracy [1.0, 1.0, 1.0]; logged regularizer [0.1724, 0.1916, 0.1445] and train loss [0.001725, 0.001916, 0.001446]" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "README.md" ], "limitations": "Only a small synthetic classification task was tested; CIFAR-10, BSD-style denoising, PSNR, artifact measurements, broad hyperparameter ablations, and matched FLOP or runtime comparisons were not performed. CUDA convolution failed in this environment, so the completed training run used CPU fallback." }