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Found 5 papers, 1 papers with code
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns.
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference
Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference.
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference
For a similar ReLU budget SENet can yield models with ~2. 32% improved classification accuracy, evaluated on CIFAR-100.
SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation
Then, given our observation that external attention (EA) presents lower PI latency than widely-adopted self-attention (SA) at the cost of accuracy, we present a selective attention search (SAS) method to integrate the strength of EA and SA.
Deep Compression of Sum-Product Networks on Tensor Networks
Sum-product networks (SPNs) represent an emerging class of neural networks with clear probabilistic semantics and superior inference speed over graphical models.
