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Reducing Retraining by Recycling Parameter-Efficient Prompts
Parameter-efficient methods are able to use a single frozen pre-trained large language model (LLM) to perform many tasks by learning task-specific soft prompts that modulate model behavior when concatenated to the input text.
Unrolled, model-based networks for lensless imaging
Various reconstruction methods are explored, on a scale from classic iterative approaches (based on the physical imaging model) to deep learned methods with many learned parameters.
Learned reconstructions for practical mask-based lensless imaging
In this work, we address these limitations using a bounded-compute, trainable neural network to reconstruct the image.
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