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Found 10 papers, 5 papers with code
Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning
We show here that the combination of a large language model and an image generation model can similarly provide useful premonitions as to how a continual learning challenge might develop over time.
RanPAC: Random Projections and Pre-trained Models for Continual Learning
In this paper, we propose a concise and effective approach for CL with pre-trained models.
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems
We found, following experiments with wide residual networks applied to the ImageNet, CIFAR 10 and CIFAR 100 image classification datasets, that BN layers do not consistently offer a significant advantage.
Ranked #94 on
Image Classification
on CIFAR-100
(using extra training data)
Diagnosing Convolutional Neural Networks using their Spectral Response
Convolutional Neural Networks (CNNs) are a class of artificial neural networks whose computational blocks use convolution, together with other linear and non-linear operations, to perform classification or regression.
Training wide residual networks for deployment using a single bit for each weight
Using wide residual networks as our main baseline, our approach simplifies existing methods that binarize weights by applying the sign function in training; we apply scaling factors for each layer with constant unlearned values equal to the layer-specific standard deviations used for initialization.
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition
We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types.
Understanding data augmentation for classification: when to warp?
In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier.
Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network
We present a neural network architecture and training method designed to enable very rapid training and low implementation complexity.
Ranked #23 on
Image Classification
on MNIST
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm
Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.
Channel noise induced stochastic facilitation in an auditory brainstem neuron model
This holds whether the firing dynamics in the model are phasic (SBSR can occur due to channel noise) or tonic (ISR can occur due to channel noise).
Neurons and Cognition Subcellular Processes
