Search Results for author:
Found 19 papers, 6 papers with code
Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach
Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels de-confounded from the environments, advancing the technical roadmap of out-of-distribution (OOD) generalization.
LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts
Thanks to the rapid development of diffusion models, unprecedented progress has been witnessed in image synthesis.
A Retrospect to Multi-prompt Learning across Vision and Language
The vision community is undergoing the unprecedented progress with the emergence of Vision-Language Pretraining Models (VLMs).
Open Set Domain Adaptation By Novel Class Discovery
In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain.
Towards a Reliable and Robust Dialogue System for Medical Automatic Diagnosis
To this end, we propose a novel DSMAD agent, INS-DS (Introspective Diagnosis System) comprising of two separate yet cooperative modules, i. e., an inquiry module for proposing symptom-inquiries and an introspective module for deciding when to inform a disease.
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues.
Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis
To eliminate this bias and inspired by the propensity score matching technique with causal diagram, we propose a propensity-based patient simulator to effectively answer unrecorded inquiry by drawing knowledge from the other records; Bias (ii) inherently comes along with the passively collected data, and is one of the key obstacles for training the agent towards "learning how" rather than "remembering what".
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples.
Ranked #19 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Graph Neural Reasoning May Fail in Certifying Boolean Unsatisfiability
It is feasible and practically-valuable to bridge the characteristics between graph neural networks (GNNs) and logical reasoning.
Graph Neural Networks for Reasoning 2-Quantified Boolean Formulas
In this paper, we target the QBF (Quantified Boolean Formula) satisfiability problem, the complexity of which is in-between propositional logic and predicate logic, and investigate the feasibility of learning GNN-based solvers and GNN-based heuristics for the cases with a universal-existential quantifier alternation (so-called 2QBF problems).
Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training.
Ranked #3 on
Multi-target Domain Adaptation
on Office-Home
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
A broad range of cross-$m$-domain generation researches boil down to matching a joint distribution by deep generative models (DGMs).
Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity
In this paper, we propose Hard Person Identity Mining (HPIM) that attempts to refine the hard example mining to improve the exploration efficacy in person re-identification.
Graph Neural Reasoning for 2-Quantified Boolean Formula Solvers
In this paper, we investigate the feasibility of learning GNN (Graph Neural Network) based solvers and GNN-based heuristics for specified QBF (Quantified Boolean Formula) problems.
FRAME Revisited: An Interpretation View Based on Particle Evolution
FRAME (Filters, Random fields, And Maximum Entropy) is an energy-based descriptive model that synthesizes visual realism by capturing mutual patterns from structural input signals.
Teaching to Teach by Structured Dark Knowledge
We evaluate T2T across different learners, teachers, and tasks, which significantly demonstrates that structured knowledge can be inherited by the teachers to further benefit learners' training.
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
The proposed process can be compatible with mini-batch based training (i. e., using a batch of unlabeled or partially labeled data as a one-time input) for object detection.
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning
Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS).
