Search Results for author:
Found 37 papers, 16 papers with code
Learning to Detect Open Classes for Universal Domain Adaptation
The new transferability measure accurately quantifies the inclination of a target example to the open classes.
Ranked #5 on
Universal Domain Adaptation
on DomainNet
Out-of-Dynamics Imitation Learning from Multimodal Demonstrations
Existing imitation learning works mainly assume that the demonstrator who collects demonstrations shares the same dynamics as the imitator.
Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations
Specifically, we design a masked policy network with a binary mask to block certain modalities.
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
The proposed framework can be trained end-to-end with the target task-specific loss, where it learns to explore better pathway configurations and exploit the knowledge in pre-trained models for each target datum.
MetaSets: Meta-Learning on Point Sets for Generalizable Representations
It is a challenging problem due to the substantial geometry shift from simulated to real data, such that most existing 3D models underperform due to overfitting the complete geometries in the source domain.
From Big to Small: Adaptive Learning to Partial-Set Domains
Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains.
Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction
At a certain time, to forecast a reasonable future trajectory, each agent needs to pay attention to the interactions with only a small group of most relevant agents instead of unnecessarily paying attention to all the other agents.
Weakly Supervised Correspondence Learning
Correspondence learning is a fundamental problem in robotics, which aims to learn a mapping between state, action pairs of agents of different dynamics or embodiments.
Learning from Imperfect Demonstrations via Adversarial Confidence Transfer
Existing learning from demonstration algorithms usually assume access to expert demonstrations.
Learning Feasibility to Imitate Demonstrators with Different Dynamics
The goal of learning from demonstrations is to learn a policy for an agent (imitator) by mimicking the behavior in the demonstrations.
Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality
Our results show that CAIL significantly outperforms other imitation learning methods from demonstrations with varying optimality.
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift.
Multi-Agent Imitation Learning with Copulas
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions, which is essential for understanding physical, social, and team-play systems.
Zoo-Tuning: Adaptive Transfer from a Zoo of Models
We propose \emph{Zoo-Tuning} to address these challenges, which learns to adaptively transfer the parameters of pretrained models to the target task.
Transferable Query Selection for Active Domain Adaptation
Due to the domain shift, the query selection criteria of prior active learning methods may be ineffective to select the most informative target samples for annotation.
MetaSets：Meta-Learning on Point Sets for Generalizable Representations
It is a challenging problem due to the substantial geometry shift from simulated to real data, such that most existing 3D models underperform due to overfitting the complete geometries in the source domain.
Open Domain Generalization with Domain-Augmented Meta-Learning
Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable.
Learning from Imperfect Demonstrations from Agents with Varying Dynamics
The proposed score enables learning from more informative demonstrations, and disregarding the less relevant demonstrations.
Transfer Reinforcement Learning across Homotopy Classes
The ability for robots to transfer their learned knowledge to new tasks -- where data is scarce -- is a fundamental challenge for successful robot learning.
Transfer Reinforcement Learning
Robotics
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
To address driving in near-accident scenarios, we propose a hierarchical reinforcement and imitation learning (H-ReIL) approach that consists of low-level policies learned by IL for discrete driving modes, and a high-level policy learned by RL that switches between different driving modes.
Robust Learning Through Cross-Task Consistency
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction
In addition, we introduce a new dataset designed specifically for autonomous-driving scenarios in areas with dense pedestrian populations: the Stanford-TRI Intent Prediction (STIP) dataset.
Adversarial Cross-Domain Action Recognition with Co-Attention
Action recognition has been a widely studied topic with a heavy focus on supervised learning involving sufficient labeled videos.
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available.
Few-Shot Video Classification via Temporal Alignment
In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that can learn to classify a previous unseen video.
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain.
Learning to Transfer Examples for Partial Domain Adaptation
Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer.
Ranked #4 on
Partial Domain Adaptation
on ImageNet-Caltech
Multi-Adversarial Domain Adaptation
Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains.
Ranked #24 on
Domain Adaptation
on Office-31
Deep Priority Hashing
Deep hashing enables image retrieval by end-to-end learning of deep representations and hash codes from training data with pairwise similarity information.
Partial Adversarial Domain Adaptation
We present Partial Adversarial Domain Adaptation (PADA), which simultaneously alleviates negative transfer by down-weighing the data of outlier source classes for training both source classifier and domain adversary, and promotes positive transfer by matching the feature distributions in the shared label space.
Ranked #3 on
Partial Domain Adaptation
on DomainNet
Transfer Adversarial Hashing for Hamming Space Retrieval
Existing work on deep hashing assumes that the database in the target domain is identically distributed with the training set in the source domain.
3D Object Classification via Spherical Projections
Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neural network to classify the spherical projection.
Partial Transfer Learning with Selective Adversarial Networks
Existing domain adversarial networks assume fully shared label space across domains.
Conditional Adversarial Domain Adaptation
Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation.
Ranked #6 on
Domain Adaptation
on USPS-to-MNIST
HashNet: Deep Learning to Hash by Continuation
Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality.
Transitive Hashing Network for Heterogeneous Multimedia Retrieval
Hashing has been widely applied to large-scale multimedia retrieval due to the storage and retrieval efficiency.
Learning Multiple Tasks with Multilinear Relationship Networks
Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks.
