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This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at Luma AI.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Luma AI during technical interviews.

Implement and visualize in-place augmentations

Quick Overview

This question evaluates a candidate's competence in designing reproducible, in-place-safe data augmentation pipelines for grayscale image denoising, including paired geometry-preserving transforms, input-only corruptions, seeding strategies, and visualization, and tests understanding of autograd/in-place safety and worker-level determinism in a Machine Learning / Computer Vision context. It is commonly asked to assess practical engineering skills for data preprocessing and model robustness, experiment reproducibility, and trade-offs between performance, memory (in-place) operations, and correctness, placing it at a practical application level rather than purely conceptual.

Task: Build a Reproducible Augmentation Pipeline for Grayscale Digit Denoising

Context

You are training a denoising model on grayscale digit images (e.g., MNIST-like, shape [B, 1, H, W]). The model should learn to reconstruct a clean image from a corrupted input. To improve robustness and generalization, implement a data augmentation pipeline that:

Applies geometry-preserving transforms identically to both input and target (paired transforms), so the supervised target remains aligned.
Applies corruption-only transforms to the input branch to simulate noise, while keeping the target branch clean.
Uses in-place operations where it is safe and appropriate.
Is seeded and structured for reproducibility.
Includes a quick visualization to compare a batch before/after augmentation.

Requirements

Implement approximately 10 augmentations suitable for grayscale digits for denoising training, including:
- Geometry (paired): random resize/scale jitter, random crop, rotations, translations, flips, affine shear, elastic distortion.
- Corruptions (input-only): brightness/contrast jitter, Gaussian noise, cutout/random erasing.
Use in-place operations where they are safe; explain where in-place is unsafe (autograd and aliasing concerns).
Provide before/after visualizations for a batch: Original vs Clean (paired-transformed) vs Noisy (input-only corrupted).
Show how to seed and structure the pipeline for reproducibility (global seeds, per-worker seeding, generators, deterministic algorithms).
You may assume PyTorch/torchvision and standard plotting tools are available. Keep the code concise and suitable for a technical screen under time constraints.

Quick Overview

Context

Applies geometry-preserving transforms identically to both input and target (paired transforms), so the supervised target remains aligned.

Applies corruption-only transforms to the input branch to simulate noise, while keeping the target branch clean.

Uses in-place operations where it is safe and appropriate.

Is seeded and structured for reproducibility.

Includes a quick visualization to compare a batch before/after augmentation.

Requirements

Implement approximately 10 augmentations suitable for grayscale digits for denoising training, including:

Geometry (paired): random resize/scale jitter, random crop, rotations, translations, flips, affine shear, elastic distortion.
Corruptions (input-only): brightness/contrast jitter, Gaussian noise, cutout/random erasing.

Use in-place operations where they are safe; explain where in-place is unsafe (autograd and aliasing concerns).

Provide before/after visualizations for a batch: Original vs Clean (paired-transformed) vs Noisy (input-only corrupted).

Show how to seed and structure the pipeline for reproducibility (global seeds, per-worker seeding, generators, deterministic algorithms).

You may assume PyTorch/torchvision and standard plotting tools are available. Keep the code concise and suitable for a technical screen under time constraints.

Implement and visualize in-place augmentations

Quick Overview

Task: Build a Reproducible Augmentation Pipeline for Grayscale Digit Denoising

Context

Requirements

Solution

Comments (0)

Implement and visualize in-place augmentations

Quick Overview

Task: Build a Reproducible Augmentation Pipeline for Grayscale Digit Denoising

Context

Requirements

Solution

Comments (0)