Chaos and Order https://www.youngju.dev/blog 천천히 올바르게. AI Researcher & DevOps Engineer Youngju's blog. GPU/CUDA, LLM, MLOps, Kubernetes AI workloads, and data engineering — plus mindset essays on confidence, routines, health, and sport psychology. ko fjvbn2003@gmail.com (Youngju Kim) fjvbn2003@gmail.com (Youngju Kim) Sun, 23 Aug 2026 00:00:00 GMT https://www.youngju.dev/blog/ai/tiny-models-05-image-to-image.en AI for Everyone, Part 5 — Colourising Photos With a 0.47M U-Net, and Why the Colours Came Out Washed Out https://www.youngju.dev/blog/ai/tiny-models-05-image-to-image.en The smallest model in this series — a 472K-parameter U-Net — restored colour to greyscale CIFAR-10 images. Shapes survived intact, but the colours came out noticeably washed out. That is not a capacity problem; it is a consequence of choosing L1 loss. We look at what skip connections carry, and why a regression loss drains saturation, using the actual output images. Sun, 23 Aug 2026 00:00:00 GMT fjvbn2003@gmail.com (Youngju Kim) aicomputer-visionunetcolorizationpytorchtiny-models https://www.youngju.dev/blog/ai/tiny-models-05-image-to-image.ja みんなのためのAI 第5回 — 47万パラメータの U-Net で白黒写真に色をつける、そしてなぜ色が褪せたのか https://www.youngju.dev/blog/ai/tiny-models-05-image-to-image.ja シリーズ最小の47万パラメータ U-Net で CIFAR-10 の白黒画像をカラーに復元しました。形は正確に保たれましたが色が目に見えて褪せています。これはモデルの容量の問題ではなく L1 損失を選んだ結果です。skip connection が何を運ぶのか、そして回帰損失がなぜ彩度を殺すのかを実際の結果画像で確認します。 Sun, 23 Aug 2026 00:00:00 GMT fjvbn2003@gmail.com (Youngju Kim) aicomputer-visionunetcolorizationpytorchtiny-models https://www.youngju.dev/blog/ai/tiny-models-05-image-to-image 모두를 위한 AI 5편 — 47만 파라미터 U-Net으로 흑백 사진에 색 입히기, 그리고 왜 색이 바랬는가 https://www.youngju.dev/blog/ai/tiny-models-05-image-to-image 시리즈에서 가장 작은 모델인 47만 파라미터 U-Net으로 CIFAR-10 흑백 이미지를 컬러로 복원했습니다. 형태는 정확히 살렸지만 색이 눈에 띄게 바랬는데, 이건 모델 용량 문제가 아니라 L1 손실을 고른 결과입니다. skip connection이 무엇을 나르는지, 그리고 회귀 손실이 왜 채도를 죽이는지를 실제 결과 이미지로 확인합니다. Sun, 23 Aug 2026 00:00:00 GMT fjvbn2003@gmail.com (Youngju Kim) aicomputer-visionunetcolorizationpytorchtiny-models https://www.youngju.dev/blog/ai/tiny-models-05-image-to-image.zh 人人可懂的 AI 第5篇 — 用47万参数的 U-Net 给黑白照片上色，以及颜色为何发灰 https://www.youngju.dev/blog/ai/tiny-models-05-image-to-image.zh 本系列最小的模型 —— 47万参数的 U-Net —— 把 CIFAR-10 的黑白图像还原成彩色。形状保留得很好，颜色却明显发灰。这不是容量问题，而是选用 L1 损失的必然结果。我们用真实输出图像说明 skip connection 究竟运送了什么，以及回归损失为何会抽干饱和度。 Sun, 23 Aug 2026 00:00:00 GMT fjvbn2003@gmail.com (Youngju Kim) aicomputer-visionunetcolorizationpytorchtiny-models