<ul><li>The study focuses on neural collapse phenomenon in deep neural networks and its implications in modern architectures like ResNets and Transformers.</li><li>Existing research has primarily been on data-agnostic models, but this paper analyzes data-aware models, proving that global optima of deep regularized transformers and ResNets exhibit neural collapse.</li><li>The research demonstrates that neural collapse becomes more pronounced as the depth of the networks increases in computer vision and language datasets.</li><li>Theoretical results suggest that deep ResNets and transformers' training can be reduced to an equivalent unconstrained features model, reinforcing their widespread applicability in various settings.</li></ul>

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers

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