Infinite dSprites for Disentangled Continual Learning: Separating Memory
Edits from Generalization

Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization

27 December 2023

Sebastian Dziadzio

cCaugatay Yildiz

Gido M. van de Ven

Tomasz Trzciñski

Tinne Tuytelaars

Matthias Bethge

Papers citing "Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization"

6 / 6 papers shown

Title
Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli Matthias Tangemann Matthias Kümmerer Matthias Bethge VOS 47 0 0 03 Nov 2024
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning A. Panos Yuriko Kobe Daniel Olmeda Reino Rahaf Aljundi Richard Turner CLL 103 42 0 23 Mar 2023
CLAD: A realistic Continual Learning benchmark for Autonomous Driving Eli Verwimp Kuo Yang Sarah Parisot Hong Lanqing Jingyu Sun Eduardo Pérez-Pellitero Matthias De Lange Tinne Tuytelaars 40 46 0 07 Oct 2022
ImageNet-21K Pretraining for the Masses T. Ridnik Emanuel Ben-Baruch Asaf Noy Lihi Zelnik-Manor SSeg VLM CLIP 181 689 0 22 Apr 2021
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups Marc Finzi Max Welling A. Wilson 79 185 0 19 Apr 2021
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,217 0 01 Sep 2014