PADL is shown equivalent to MAP estimation under a structured generative model, yielding generalization guarantees, an analytical sparsity-storage-accuracy tradeoff, and a tuning-free algorithm tested on visual benchmarks and vision-language model acceleration.
Greedy Deep Dictionary Learning
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abstract
In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning tools like discriminative KSVD and label consistent KSVD. Our method yields better results than all.
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cs.LG 1years
2026 1verdicts
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On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning
PADL is shown equivalent to MAP estimation under a structured generative model, yielding generalization guarantees, an analytical sparsity-storage-accuracy tradeoff, and a tuning-free algorithm tested on visual benchmarks and vision-language model acceleration.