Mapping Networks

arXiv:2602.19134 · cs.CV · Submitted 2026-02-22 · Read on arXiv

cs.CV

Submitted: 2026-02-22

Updated: 2026-02-22

Terminology

• Accuracy — 18× in this paper · explained in 1 episode
• Pruning — 8× in this paper · explained in 4 episodes
• Hypernetwork — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Latent Space — 4× in this paper · explained in 7 episodes
• Loss Function — 4× in this paper · explained in 2 episodes
• Intrinsic Dimension — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Low Rank Decomposition — 2× in this paper
• Quantization — 2× in this paper · explained in 12 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Single Latent Vector Training — 1× in this paper

Sources

• Speeding up Convolutional Neural Networks with Low Rank Expansions
• Measuring the Intrinsic Dimension of Objective Landscapes
• The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

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