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Found 6 papers, 2 papers with code
Accelerated Inference and Reduced Forgetting: The Dual Benefits of Early-Exit Networks in Continual Learning
We assess the accuracy and computational cost of various continual learning techniques enhanced with early-exits and TLC across standard class-incremental learning benchmarks such as 10 split CIFAR100 and ImageNetSubset and show that TLC can achieve the accuracy of the standard methods using less than 70\% of their computations.
SADMoE: Exploiting Activation Sparsity with Dynamic-k Gating
Transformer models, despite their impressive performance, often face practical limitations due to their high computational requirements.
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting.
Hypernetworks build Implicit Neural Representations of Sounds
Implicit Neural Representations (INRs) are nowadays used to represent multimedia signals across various real-life applications, including image super-resolution, image compression, or 3D rendering.
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
Implicit neural representations (INRs) are a rapidly growing research field, which provides alternative ways to represent multimedia signals.
Progressive Latent Replay for efficient Generative Rehearsal
We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network.
