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Found 11 papers, 2 papers with code
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data.
Text-driven Prompt Generation for Vision-Language Models in Federated Learning
Prompt learning for vision-language models, e. g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons.
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability
Raw deep neural network (DNN) performance is not enough; in real-world settings, computational load, training efficiency and adversarial security are just as or even more important.
Slimming Neural Networks using Adaptive Connectivity Scores
Finally, we define a novel sensitivity criterion for filters that measures the strength of their contributions to the succeeding layer and highlights critical filters that need to be completely protected from pruning.
MINT: Deep Network Compression via Mutual Information-based Neuron Trimming
Most approaches to deep neural network compression via pruning either evaluate a filter's importance using its weights or optimize an alternative objective function with sparsity constraints.
Rethinking Curriculum Learning with Incremental Labels and Adaptive Compensation
In this work, we propose Learning with Incremental Labels and Adaptive Compensation (LILAC), a two-phase method that incrementally increases the number of unique output labels rather than the difficulty of samples while consistently using the entire dataset throughout training.
ViP: Video Platform for PyTorch
This work presents the Video Platform for PyTorch (ViP), a deep learning-based framework designed to handle and extend to any problem domain based on videos.
A Geometric Approach to Online Streaming Feature Selection
In this work, firstly, we assert that OSFS's main assumption of having data from all the samples available at runtime is unrealistic and introduce a new setting where features and samples are streamed concurrently called OSFS with Streaming Samples (OSFS-SS).
M-PACT: An Open Source Platform for Repeatable Activity Classification Research
We present M-PACT to overcome existing issues by removing the need to develop boilerplate code which allows users to quickly prototype action classification models while leveraging existing state-of-the-art (SOTA) models available in the platform.
T-RECS: Training for Rate-Invariant Embeddings by Controlling Speed for Action Recognition
We explore the erratic behavior caused by this phenomena on state-of-the-art deep network-based methods for action recognition in terms of maximum performance and stability in recognition accuracy across a range of input video speeds.
Spatiotemporal Articulated Models for Dynamic SLAM
We propose an online spatiotemporal articulation model estimation framework that estimates both articulated structure as well as a temporal prediction model solely using passive observations.
