Mode Connectivity Beyond Classifiers: Evidence from Generative and Contrastive Models

arXiv:2608.30366 · cs.LG · Submitted 2026-08-31 · Read on arXiv

cs.LG

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/amaralibey/nanoCLIP

Terminology

• Adam — 10× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Ablation — 5× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Fisher Information — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Denoising Diffusion Probabilistic Models — 1× in this paper
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Low-Loss Path Finding — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Stochastic Gradient Descent — 1× in this paper
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes

Sources

• Escaping the Big Data Paradigm with Compact Transformers
• Denoising Diffusion Probabilistic Models
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Vanishing Variance Problem in Fully Decentralized Neural-Network Systems

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