All you need is a good init
Paper
• 1511.06422 • Published
• 1
Papers - Image - Datasets - CIFAR
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Align Your Steps: Optimizing Sampling Schedules in Diffusion Models
Paper • 2404.14507 • Published • 23 -
Deep Residual Learning for Image Recognition
Paper • 1512.03385 • Published • 12 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 15 -
Deep Learning is Robust to Massive Label Noise
Paper • 1705.10694 • Published • 1 -
Poincaré ResNet
Paper • 2303.14027 • Published • 1 -
Fixup Initialization: Residual Learning Without Normalization
Paper • 1901.09321 • Published • 1 -
RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness
Paper • 2206.14502 • Published • 1 -
SLIP: Self-supervision meets Language-Image Pre-training
Paper • 2112.12750 • Published • 1 -
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Paper • 2407.21794 • Published • 6 -
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 43