arXiv:2112.10638
공식 코드 · Papers with Code
Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models
원문 초록
Latte (for LATent Tensor Evaluation) is a Python library for evaluation of latent-based generative models in the fields of disentanglement learning and controllable generation. Latte is compatible with both PyTorch and TensorFlow/Keras, and provides both functional and modular APIs that can be easily extended to support other deep learning frameworks. Using NumPy-based and framework-agnostic implementation, Latte ensures reproducible, consistent, and deterministic metric calculations regardless of the deep learning framework of choice.
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Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
Dual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering
Instruction-Tuned Video-Audio Models Elucidate Functional Specialization in the Brain
Revisiting Cross-Modal Knowledge Distillation: A Disentanglement Approach for RGBD Semantic Segmentation
Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models
Interpretability Illusions with Sparse Autoencoders: Evaluating Robustness of Concept Representations
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