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Found 6 papers, 4 papers with code
EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods
From both the human study and automated evaluation, we find that: (i) Instruct-Pix2Pix, Null-Text and SINE are the top-performing methods averaged across different edit types, however {\it only} Instruct-Pix2Pix and Null-Text are able to preserve original image properties; (ii) Most of the editing methods fail at edits involving spatial operations (e. g., changing the position of an object).
Identifying Interpretable Subspaces in Image Representations
Although many existing approaches interpret features independently, we observe in state-of-the-art self-supervised and supervised models, that less than 20% of the representation space can be explained by individual features.
Efficient Video Classification Using Fewer Frames
We focus on building compute-efficient video classification models which process fewer frames and hence have less number of FLOPs.
Ranked #2 on
Video Classification
on YouTube-8M
Studying the Plasticity in Deep Convolutional Neural Networks using Random Pruning
In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned.
I Have Seen Enough: A Teacher Student Network for Video Classification Using Fewer Frames
We then train a student network whose objective is to process only a small fraction of the frames in the video and still produce a representation which is very close to the representation computed by the teacher network.
Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks
In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned.
