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Found 21 papers, 4 papers with code
Cost-Aware Evaluation and Model Scaling for LiDAR-Based 3D Object Detection
Considerable research efforts have been devoted to LiDAR-based 3D object detection and its empirical performance has been significantly improved.
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains
Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks.
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization
Multi-task regression attempts to exploit the task similarity in order to achieve knowledge transfer across related tasks for performance improvement.
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems
Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems.
Neighborhood-Aware Neural Architecture Search
To facilitate the application to gradient-based algorithms, we also propose a differentiable representation for the neighborhood of architectures.
Efficient Model Performance Estimation via Feature Histories
Using a sampling-based search algorithm and parallel computing, our method can find an architecture which is better than DARTS and with an 80% reduction in wall-clock search time.
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models
Committee-based models (ensembles or cascades) construct models by combining existing pre-trained ones.
Modulating Scalable Gaussian Processes for Expressive Statistical Learning
For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also the associated variability.
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification
The discovered attention cells can be seamlessly inserted into existing backbone networks, e. g., I3D or S3D, and improve video classification accuracy by more than 2% on both Kinetics-600 and MiT datasets.
Deep Latent-Variable Kernel Learning
Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model.
Point in, Box out: Beyond Counting Persons in Crowds
In the end, we propose a curriculum learning strategy to train the network from images of relatively accurate and easy pseudo ground truth first.
Learnable Embedding Space for Efficient Neural Architecture Compression
We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training.
Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning
In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i. e. learning from scratch all the time).
Error Correction Maximization for Deep Image Hashing
We propose to use the concept of the Hamming bound to derive the optimal criteria for learning hash codes with a deep network.
Image Registration Based Flicker Solving in Video Face Replacement and Analysis Based Sub-pixel Image Registration
In this paper, a framework of video face replacement is proposed and it deals with the flicker of swapped face in video sequence.
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations.
Discriminative and Geometry Aware Unsupervised Domain Adaptation
Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions.
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation
Domain adaptation (DA) is transfer learning which aims to leverage labeled data in a related source domain to achieve informed knowledge transfer and help the classification of unlabeled data in a target domain.
Close Yet Distinctive Domain Adaptation
Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions.
Deep Supervised Hashing with Triplet Labels
The current state-of-the-art deep hashing method DPSH~\cite{li2015feature}, which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities.
Contextual Visual Similarity
Given a query image, a second positive image and a third negative image, dissimilar to the first two images, we define a contextualized similarity search criteria.
