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Found 5 papers, 1 papers with code
E2Net: Resource-Efficient Continual Learning with Elastic Expansion Network
In E2Net, we propose Representative Network Distillation to identify the representative core subnet by assessing parameter quantity and output similarity with the working network, distilling analogous subnets within the working network to mitigate reliance on rehearsal buffers and facilitating knowledge transfer across previous tasks.
eTag: Class-Incremental Learning with Embedding Distillation and Task-Oriented Generation
Most successful CIL methods incrementally train a feature extractor with the aid of stored exemplars, or estimate the feature distribution with the stored prototypes.
Towards Understanding the Generalization of Deepfake Detectors from a Game-Theoretical View
Deepfake detectors encode multi-order interactions among visual concepts, in which the low-order interactions usually have substantially negative contributions to deepfake detection.
PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration
Deep learning has achieved impressive results in many areas, but the deployment of edge intelligent devices is still very slow.
Multi-Objective Pruning for CNNs Using Genetic Algorithm
In this work, we propose a heuristic genetic algorithm (GA) for pruning convolutional neural networks (CNNs) according to the multi-objective trade-off among error, computation and sparsity.
