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Found 22 papers, 2 papers with code
Scalable Weight Reparametrization for Efficient Transfer Learning
Efficient transfer learning involves utilizing a pre-trained model trained on a larger dataset and repurposing it for downstream tasks with the aim of maximizing the reuse of the pre-trained model.
Quadapter: Adapter for GPT-2 Quantization
In that case, it is observed that quantization-aware training overfits the model to the fine-tuning data.
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting
We also verify our method on a standard benchmark, miniImageNet, and D-ProtoNets shows the state-of-the-art open-set detection rate in FSOSR.
Personalized Keyword Spotting through Multi-task Learning
We design two personalized KWS tasks; (1) Target user Biased KWS (TB-KWS) and (2) Target user Only KWS (TO-KWS).
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design
The goal of the task is to design an audio scene classification system for device-imbalanced datasets under the constraints of model complexity.
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification
While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features.
Distribution Estimation to Automate Transformation Policies for Self-Supervision
However, it is observed that image transformations already present in the dataset might be less effective in learning such self-supervised representations.
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization
Moreover, we introduce an efficient architecture, BC-ResNet-ASC, a modified version of the baseline architecture with a limited receptive field.
Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation
While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational and memory efficiency.
Self-Evolutionary Optimization for Pareto Front Learning
Utilizing SEO for PFL, we also introduce self-evolutionary Pareto networks (SEPNet), enabling the unified model to approximate the entire Pareto front set that maximizes the hypervolume.
Towards Robust Domain Generalization in 2D Neural Audio Processing
While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features.
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation
Unlike traditional pruning and KD, PQK makes use of unimportant weights pruned in the pruning process to make a teacher network for training a better student network without pre-training the teacher model.
Broadcasted Residual Learning for Efficient Keyword Spotting
We present a broadcasted residual learning method to achieve high accuracy with small model size and computational load.
Ranked #2 on
Keyword Spotting
on Google Speech Commands
Prototype-based Personalized Pruning
We verify the usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.
SubSpectral Normalization for Neural Audio Data Processing
In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group.
Ranked #1 on
Keyword Spotting
on TAU Urban Acoustic Scenes 2019
URNet : User-Resizable Residual Networks with Conditional Gating Module
There are methods to reduce the cost by compressing networks or varying its computational path dynamically according to the input image.
Genetic-Gated Networks for Deep Reinforcement Learning
We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks.
Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation
Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network.
Genetic-Gated Networks for Deep Reinforcement
We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks.
Towards Governing Agent's Efficacy: Action-Conditional $β$-VAE for Deep Transparent Reinforcement Learning
We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way.
Broadcasting Convolutional Network for Visual Relational Reasoning
In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features.
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning
Our method learns hundreds to thousand times faster than the conventional methods by learning only a handful of core cluster information, which shows that deep RL agents can effectively learn through the shared knowledge from other agents.
