Papers Exemplar-Free
“Exemplar-Free” 태그가 달린 논문 72편 · 필터 해제
Addressing The Devastating Effects Of Single-Task Data Poisoning In Exemplar-Free Continual Learning
Our research addresses the overlooked security concerns related to data poisoning in continual learning (CL). Data poisoning - the intentional manipulation of training data to affect the predictions of machine learning m…
Continual LearningData PoisoningExemplar-FreeDuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic
Real-world object detection systems, such as those in autonomous driving and surveillance, must continuously learn new object categories and simultaneously adapt to changing environmental conditions. Existing approaches,…
Autonomous DrivingAvgClass-Incremental Object DetectionExemplar-Free+5L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Class-incremental learning (CIL) enables models to learn new classes continually without forgetting previously acquired knowledge. Multi-label CIL (MLCIL) extends CIL to a real-world scenario where each sample may belong…
class-incremental learningClass Incremental LearningExemplar-FreeIncremental Learning+2Exemplar-Free Continual Learning for State Space Models
State-Space Models (SSMs) excel at capturing long-range dependencies with structured recurrence, making them well-suited for sequence modeling. However, their evolving internal states pose challenges in adapting them und…
Continual LearningExemplar-FreeState Space ModelsListen, Analyze, and Adapt to Learn New Attacks: An Exemplar-Free Class Incremental Learning Method for Audio Deepfake Source Tracing
As deepfake speech becomes common and hard to detect, it is vital to trace its source. Recent work on audio deepfake source tracing (ST) aims to find the origins of synthetic or manipulated speech. However, ST models mus…
class-incremental learningClass Incremental LearningContinual LearningDeepFake Detection+3StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning
Video Class-Incremental Learning (VCIL) seeks to develop models that continuously learn new action categories over time without forgetting previously acquired knowledge. Unlike traditional Class-Incremental Learning (CIL…
class-incremental learningClass Incremental LearningExemplar-FreeIncremental Learning+1AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
Keyword spotting (KWS) offers a vital mechanism to identify spoken commands in voice-enabled systems, where user demands often shift, requiring models to learn new keywords continually over time. However, a major problem…
class-incremental learningClass Incremental LearningContinual LearningExemplar-Free+3Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification
Lifelong Person Re-identification (LReID) suffers from a key challenge in preserving old knowledge while adapting to new information. The existing solutions include rehearsal-based and rehearsal-free methods to address t…
Exemplar-FreeKnowledge DistillationLifelong learningMixture-of-Experts+4Audio-Visual Class-Incremental Learning for Fish Feeding intensity Assessment in Aquaculture
Fish Feeding Intensity Assessment (FFIA) is crucial in industrial aquaculture management. Recent multi-modal approaches have shown promise in improving FFIA robustness and efficiency. However, these methods face signific…
Benchmarkingclass-incremental learningClass Incremental LearningExemplar-Free+1Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-Training
Existing class-incremental learning methods in 3D point clouds rely on exemplars (samples of former classes) to resist the catastrophic forgetting of models, and exemplar-free settings will greatly degrade the performanc…
3D geometryclass-incremental learningClass Incremental LearningExemplar-Free+1LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning
In continual learning (CL), catastrophic forgetting often arises due to feature drift. This challenge is particularly prominent in the exemplar-free continual learning (EFCL) setting, where samples from previous tasks ca…
Continual LearningExemplar-Freeparameter-efficient fine-tuningTripletEFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning
Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient da…
class-incremental learningClass Incremental LearningExemplar-FreeIncremental LearningSemantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning
Exemplar-Free Class-Incremental Learning (EFCIL) aims to sequentially learn from distinct categories without retaining exemplars but easily suffers from catastrophic forgetting of learned knowledge. While existing EFCIL …
class-incremental learningClass Incremental LearningExemplar-FreeIncremental Learning+1Incremental Learning with Repetition via Pseudo-Feature Projection
Incremental Learning scenarios do not always represent real-world inference use-cases, which tend to have less strict task boundaries, and exhibit repetition of common classes and concepts in their continual data stream.…
Exemplar-FreeIncremental LearningConSense: Continually Sensing Human Activity with WiFi via Growing and Picking
WiFi-based human activity recognition (HAR) holds significant application potential across various fields. To handle dynamic environments where new activities are continuously introduced, WiFi-based HAR systems must adap…
Activity Recognitionclass-incremental learningClass Incremental LearningExemplar-Free+2On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning
Exemplar-free class incremental learning (EF-CIL) is a nontrivial task that requires continuously enriching model capability with new classes while maintaining previously learned knowledge without storing and replaying a…
class-incremental learningClass Incremental LearningExemplar-FreeIncremental LearningPAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning
Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs, thereby enabling models to learn continuously across a sequence of tasks while mitigating forg…
class-incremental learningClass Incremental LearningExemplar-FreeIncremental LearningCSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free Video Class-Incremental Learning
Continual learning aims to acquire new knowledge while retaining past information. Class-incremental learning (CIL) presents a challenging scenario where classes are introduced sequentially. For video data, the task beco…
class-incremental learningClass Incremental LearningContinual LearningExemplar-Free+2Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning
We propose Adapter Merging with Centroid Prototype Mapping (ACMap), an exemplar-free framework for class-incremental learning (CIL) that addresses both catastrophic forgetting and scalability. While existing methods trad…
class-incremental learningClass Incremental LearningExemplar-FreeIncremental LearningDASK: Distribution Rehearsing via Adaptive Style Kernel Learning for Exemplar-Free Lifelong Person Re-Identification
Lifelong person re-identification (LReID) is an important but challenging task that suffers from catastrophic forgetting due to significant domain gaps between training steps. Existing LReID approaches typically rely on …
Exemplar-FreeKnowledge DistillationPerson Re-IdentificationStyle Transfer