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Awesome GitHub RepositoriesSpace Shaping Regularizers
Penalties such as center invariance or sparsity that shape the distribution of learned embeddings.
Distinct from Embedding Regularization: Distinct from Embedding Regularization: focuses on shaping the overall embedding space distribution (center invariance, sparsity) rather than noise injection or general constraints.
Explore 1 awesome GitHub repository matching artificial intelligence & ml · Space Shaping Regularizers. Refine with filters or upvote what's useful.
Awesome Space Shaping Regularizers GitHub Repositories
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- kevinmusgrave/pytorch-metric-learning
KevinMusgrave/pytorch-metric-learning
6,328عرض على GitHubPyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving embedding spaces. It provides a modular framework where interchangeable loss functions, mining strategies, and evaluation tools can be composed to learn representations that map similar items to nearby points and dissimilar items to distant points in the embedding space. The library distinguishes itself through a highly configurable architecture that separates concerns across several interchangeable components. Users can assemble custom loss functions from pluggable distance metrics
Applies center invariance and sparsity penalties to shape the learned embedding space distribution.
Pythoncomputer-visioncontrastive-learningdeep-learning
