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Found 13 papers, 3 papers with code
Divide and not forget: Ensemble of selectively trained experts in Continual Learning
Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know.
Creating New Voices using Normalizing Flows
As there is growing interest in synthesizing voices of new speakers, here we investigate the ability of normalizing flows in text-to-speech (TTS) and voice conversion (VC) modes to extrapolate from speakers observed during training to create unseen speaker identities.
Revisiting Supervision for Continual Representation Learning
In the field of continual learning, models are designed to learn tasks one after the other.
Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation
Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence.
AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation
Test-time adaptation is a promising research direction that allows the source model to adapt itself to changes in data distribution without any supervision.
Looking through the past: better knowledge retention for generative replay in continual learning
We notice that in VAE-based generative replay, this could be attributed to the fact that the generated features are far from the original ones when mapped to the latent space.
Cross-lingual Knowledge Distillation via Flow-based Voice Conversion for Robust Polyglot Text-To-Speech
In the first two stages, we use a VC model to convert utterances in the target locale to the voice of the target speaker.
Generalized Continual Category Discovery
However, these settings are not well aligned with real-life scenarios, where a learning agent has access to a vast amount of unlabeled data encompassing both novel (entirely unlabeled) classes and examples from known classes.
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting.
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space.
Robust Object Detection with Multi-input Multi-output Faster R-CNN
In this work, a generalization of the MIMO approach is applied to the task of object detection using the general-purpose Faster R-CNN model.
Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift
While recent computer vision algorithms achieve impressive performance on many benchmarks, they lack robustness - presented with an image from a different distribution, (e. g. weather or lighting conditions not considered during training), they may produce an erroneous prediction.
Robustness in Compressed Neural Networks for Object Detection
It was further shown that while data imbalance methods brought only a slight increase in accuracy for the baseline model (without compression), the impact was more striking at higher compression rates for the structured pruning.
